Controlling Intersample Analyte Variability in Complex Biological Matrices

ABSTRACT

Described herein are compositions and methods for normalizing the variability of inter-sample analyte measurements from a biological matrix. In some embodiments, the present disclosure relates to methods for normalizing the levels of one or more proteins from urine as measured by an aptamer based assay.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 62/849,212, filed May 17, 2019, which is incorporated byreference in its entirety for any purpose.

FIELD

The present disclosure relates generally to controlling variability ofinter-sample analyte measurements from a biological matrix. In someembodiments, the present disclosure relates to methods for controllingthe levels of one or more proteins from a urine sample as measured by anaptamer based assay.

BACKGROUND

Inter-sample variability of analyte measurement in biological samples isa problem for biomarker discovery, metabolic analyses, gene expressionanalysis, protein pathway analysis, and diagnostic and prognostic tools,particularly when the outcome relies on quantitative biological signalsthat differ by a relatively small magnitude. Biological sample typesthat exhibit high between-sample variability are a primary challenge ofworking with these sample types. Controlling for such variability wouldprovide for more consistent and meaningful datasets for experimental andclinical applications.

Therefore, there continues to be a need for alternative compositions andmethods for controlling inter-sample analyte measurement variability inbiological matrices. The present disclosure meets such needs byproviding novel compositions and methods for normalizing biologicalsignals in complex matrices, which reduce, minimize, or remove suchvariability.

SUMMARY

Embodiment 1. A method for generating a composite dilution model, themethod comprising:

-   -   a) determining the level of an analyte in a first dilution        series of a first biological sample comprising the analyte,        wherein the first dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   b) determining the levels of the analyte in a second dilution        series of a second biological sample comprising the analyte,        wherein the second dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   c) generating a model based on the levels of the analyte of the        first dilution series;    -   d) selecting a reference value, wherein the reference value is        selected from:        -   (i) a second dilution series reference value that is an            analyte level at a specific dilution of the second dilution            series;        -   (ii) a model reference value that is an analyte level at a            specific dilution of the model; and        -   (iii) an arbitrary reference value that is an analyte level            at a specific dilution, wherein the analyte level at that            specific dilution is not found in the second dilution series            or the model;    -   e) performing at least one horizontal translation, wherein the        at least one horizontal translation is selected from:        -   (i) a horizontal translation (ΔX) of the second dilution            series reference value to a model value, wherein the model            value is an analyte level at a specific dilution of the            model, wherein the second dilution series reference value            and the model value are equivalent or substantially            equivalent;        -   (ii) a horizontal translation (ΔX) of the model reference            value to a second dilution series value, wherein the second            dilution series value is an analyte level at a specific            dilution of the second dilution series, wherein the model            reference value and the second dilution series value are            equivalent or substantially equivalent; and        -   (iii) horizontal translations of a second dilution series            value (ΔX) and a model value (ΔY) to the arbitrary reference            value, wherein the model value is an analyte level at a            specific dilution of the model, wherein the second dilution            series value is an analyte level at a specific dilution of            the second dilution series, and wherein the second dilution            series value, the model value and the arbitrary reference            value are equivalent or substantially equivalent;    -   f) performing the at least one horizontal translation of the        remaining values in (i) the second dilution series, (ii) the        model, or (iii) the second dilution series and the model,        wherein the horizontal translations of the remaining values        results in a lined composite translation; and    -   g) fitting a function to the lined composite translation series        thereby forming a composite dilution model.

Embodiment 2. The method of embodiment 1, wherein the method comprises:

-   -   d) selecting a second dilution series reference value that is an        analyte level at a specific dilution of the second dilution        series;    -   e) performing a horizontal translation (ΔX) of the second        dilution series reference value to a model value, wherein the        model value is an analyte level at a specific dilution of the        model, wherein the second dilution series reference value and        the model value are equivalent or substantially equivalent; and    -   f) performing the horizontal translation of the remaining values        in the second dilution series, wherein the horizontal        translation of the remaining values results in a lined composite        translation.

Embodiment 3. The method of embodiment 1, wherein the method comprises:

-   -   d) selecting a model reference value that is an analyte level at        a specific dilution of the model;    -   e) performing a horizontal translation (ΔX) of the model        reference value to a second dilution series value, wherein the        second dilution series value is an analyte level at a specific        dilution of the second dilution series, wherein the model        reference value and the second dilution series value are        equivalent or substantially equivalent; and    -   f) performing the horizontal translation of the remaining values        in the model, wherein the horizontal translation of the        remaining values results in a lined composite translation.

Embodiment 4. The method of embodiment 1, wherein the method comprises:

-   -   d) selecting an arbitrary reference value that is an analyte        level at a specific dilution, wherein the analyte level at that        specific dilution is not found in the second dilution series or        the model;    -   e) performing a horizontal translation of a second dilution        series value (ΔX) and a model value (ΔY) to the arbitrary        reference value, wherein the model value is an analyte level at        a specific dilution of the model, wherein the second dilution        series value is an analyte level at a specific dilution of the        second dilution series, and wherein the second dilution series        value, the model value and the arbitrary reference value are        equivalent or substantially equivalent; and    -   f) performing the horizontal translations of the remaining        values in the second dilution series and the model, wherein the        horizontal translations of the remaining values results in a        lined composite translation.

Embodiment 5. A method for generating a composite dilution model, themethod comprising:

-   -   a) determining the level of an analyte in a first dilution        series of a first biological sample comprising the analyte,        wherein the first dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   b) determining the levels of the analyte in a second dilution        series of a second biological sample comprising the analyte,        wherein the second dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   c) generating a model based on the levels of the analyte of the        first dilution series;    -   d) performing at least one horizontal translation, wherein the        at least one horizontal translation is selected from:    -   (i) a horizontal translation (ΔX) of the second dilution series        to the model;    -   (ii) a horizontal translation (ΔX) of the model to the second        dilution series; and    -   (iii) horizontal translations of the second dilution series (ΔX)        and the model (ΔY) to an arbitrary reference value, wherein the        arbitrary reference value is an analyte level at a specific        dilution, wherein the analyte level at that specific dilution is        not found in the second dilution series or the model, wherein        the at least one horizontal translation results in a lined        composite translation series; and    -   e) fitting a function to the lined composite translation series        thereby forming a composite dilution model.

Embodiment 6. The method of embodiment 5, wherein the method comprisesperforming a horizontal translation (ΔX) of the second dilution seriesto the model.

Embodiment 7. The method of embodiment 5, wherein the method comprisesperforming a horizontal translation (ΔX) of the model to the seconddilution series.

Embodiment 8. The method of embodiment 5, wherein the method comprisesperforming a horizontal translation of the second dilution series (ΔX)and the model (ΔY) to an arbitrary reference value that is an analytelevel at a specific dilution, wherein the analyte level at that specificdilution is not found in the second dilution series or the model.

Embodiment 9. A method for generating a composite dilution model, themethod comprising:

-   -   a) determining the level of an analyte in a first dilution        series of a first biological sample comprising the analyte,        wherein the first dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   b) determining the levels of the analyte in a second dilution        series of a second biological sample comprising the analyte,        wherein the second dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   c) selecting a reference value, wherein the reference value is        selected from:        -   (i) a second dilution series reference value that is an            analyte level at a specific dilution of the second dilution            series; and        -   (ii) an arbitrary reference value that is an analyte level            at a specific dilution, wherein the analyte level at that            specific dilution is not found in the first dilution series            or the second dilution series;    -   d) performing at least one horizontal translation, wherein the        at least one horizontal translation is selected from:    -   (i) a horizontal translation (ΔX) of the second dilution series        reference value to a first dilution series value, wherein the        first dilution series value is an analyte level at a specific        dilution of the first dilution series, wherein the second        dilution series reference value and the first dilution series        value are equivalent or substantially equivalent; and    -   (ii) horizontal translations of the first dilution series (ΔX)        and the second dilution series (ΔY) to the arbitrary reference        value;        wherein the at least one horizontal translation results in a        lined composite translation series; and    -   e) fitting a function to the lined composite translation series        thereby forming a composite dilution model.

Embodiment 10. The method of embodiment 9, wherein the method comprisesperforming a horizontal translation (ΔX) of the second dilution seriesto the first dilution series.

Embodiment 11. The method of embodiment 9, wherein the method compriseshorizontal translations of the first dilution series (ΔX) and the seconddilution series (ΔY) to an arbitrary reference value, wherein thearbitrary reference value is an analyte level at a specific dilution,wherein the analyte level at that specific dilution is not found in thefirst dilution series or the second dilution series.

Embodiment 12. A method for generating a composite dilution model, themethod comprising:

-   -   a) determining the level of an analyte in a first dilution        series of a first biological sample comprising the analyte,        wherein the first dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   b) determining the levels of the analyte in a second dilution        series of a second biological sample comprising the analyte,        wherein the second dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   c) performing at least one horizontal translation, wherein the        at least one horizontal translation is selected from:    -   (i) a horizontal translation (ΔX) of the second dilution series        to the first dilution series; and    -   (ii) horizontal translations of the first dilution series (ΔX)        and the second dilution series (ΔY) to an arbitrary reference        value, wherein the arbitrary reference value is an analyte level        at a specific dilution, wherein the analyte level at that        specific dilution is not found in the first dilution series or        the second dilution series;        wherein the at least one horizontal translation results in a        lined composite translation series; and    -   d) fitting a function to the lined composite translation series        thereby forming a composite dilution model.

Embodiment 13. The method of embodiment 12, wherein the method comprisesperforming a horizontal translation (ΔX) of the second dilution seriesto the first dilution series.

Embodiment 14. The method of embodiment 12, wherein the method compriseshorizontal translations of the first dilution series (ΔX) and the seconddilution series (ΔY) to an arbitrary reference value, wherein thearbitrary reference value is an analyte level at a specific dilution,wherein the analyte level at that specific dilution is not found in thefirst dilution series or the second dilution series.

Embodiment 15. A method for generating a composite dilution model, themethod comprising:

-   -   a) determining the level of an analyte in a first dilution        series of a first biological sample comprising the analyte,        wherein the first dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   b) determining the levels of the analyte in a second dilution        series of a second biological sample comprising the analyte,        wherein the second dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   c) generating a first model based on the levels of the analyte        of the first dilution series and a second model based on the        levels of the analyte of the second dilution series;    -   d) selecting a reference value, wherein the reference value is        selected from:        -   (i) a first model reference value that is an analyte level            at a specific dilution of the first model; and        -   (ii) an arbitrary reference value that is an analyte level            at a specific dilution, wherein the analyte level at that            specific dilution is not found in the first model or the            second model;    -   d) performing at least one horizontal translation, wherein the        at least one horizontal translation is selected from:    -   (i) a horizontal translation (ΔX) of the first model to the        second model; and    -   (ii) horizontal translations of the first model (ΔX) and the        second model (ΔY) to the arbitrary reference value; and wherein        the horizontal translation result in a lined composite        translation series; and    -   e) fitting a function to the lined composite translation series        thereby forming a composite dilution model.

Embodiment 16. The method of embodiment 15, wherein the method comprisesperforming a horizontal translation (ΔX) of the first model to thesecond model.

Embodiment 17. The method of embodiment 15, wherein the method compriseshorizontal translations of the first model (ΔX) and the second model(ΔY) to the arbitrary reference value.

Embodiment 18. A method for generating a composite dilution model, themethod comprising:

-   -   a) determining the level of an analyte in a first dilution        series of a first biological sample comprising the analyte,        wherein the first dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   b) determining the levels of the analyte in a second dilution        series of a second biological sample comprising the analyte,        wherein the second dilution series comprises at least three (3)        different dilutions, and the level of the analyte is determined        in each of the at least three different dilutions;    -   c) generating a first model based on the levels of the analyte        of the first dilution series and a second model based on the        levels of the analyte of the second dilution series;    -   d) performing at least one horizontal translation, wherein the        at least one horizontal translation is selected from:    -   (i) a horizontal translation (ΔX) of the first model to the        second model; and    -   (ii) horizontal translations of the first model (ΔX) and the        second model (ΔY) to an arbitrary reference value, wherein the        analyte level at that specific dilution is not found in the        first model or the second model; and wherein the horizontal        translation result in a lined composite translation series; and    -   e) fitting a function to the lined composite translation series        thereby forming a composite dilution model.

Embodiment 19. The method of embodiment 18, wherein the method comprisesperforming a horizontal translation (ΔX) of the first model to thesecond model.

Embodiment 20. The method of embodiment 18, wherein the method compriseshorizontal translations of the first model (ΔX) and the second model(ΔY) to an arbitrary reference value.

Embodiment 21. The method of any one of the preceding embodiments,wherein the level of the analyte is determined in each of at least 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 different dilutions in thefirst dilution series.

Embodiment 22. The method of any one of the preceding embodiments,wherein the level of the analyte is determined in each of at least 8different dilutions.

Embodiment 23. The method of any one of the preceding embodiments,wherein the level of the analyte is determined in each of at least 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 different dilutions in thesecond dilution series.

Embodiment 24. The method of any one of the preceding embodiments,wherein the level of the analyte is determined in each of at least 8different dilutions in the second dilution series.

Embodiment 25. The method of any one of the preceding embodiments,wherein each model is independently selected from a linear regressionmodel, a LOESS curve fitting model, a non-linear regression model, aspline fit model, a mixed effects regression model, a fixed effectsregression model, a generalized linear model, a matrix decompositionmodel, and a four parameter logistic regression (4PL) model,

Embodiment 26. The method of any one of the previous embodiments,wherein the level of the analyte is the relative amount of the analyteor the analyte concentration.

Embodiment 27. The method of any one of the preceding embodiments,wherein the selected reference value is within the linear range of thedilution series or model.

Embodiment 28. The method of embodiment 21, wherein the selectedreference value is the center point of the linear range.

Embodiment 29. The method of any one of the preceding embodiments,wherein the first and second biological samples comprise urine or arederived from urine.

Embodiment 30. The method of any one of the preceding embodiments,wherein the first and second biological samples are collected from thesame subject.

Embodiment 31. The method of any one of the preceding embodiments,wherein the first and second biological samples are collected fromdifferent subjects.

Embodiment 32. The method of embodiment 31, wherein the first biologicalsample is collected at a first time point and the second biologicalsample is collected at a second time point.

Embodiment 33. The method of embodiment 32, wherein the first time pointand the second time point differ by at least about 0.5 hours, 1 hour, 2hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, 24hours, 36 hours, 48 hours, 60 hours or 72 hours.

Embodiment 34. The method of any one of the preceding embodiments,wherein the level of the analyte is measured by an assay the uses anaptamer, antibody, mass spectrophotometer or a combination thereof.

Embodiment 35. The method of any one of preceding embodiments, whereinthe dilution factor for each of the first dilution series and seconddilution series is a constant dilution factor.

Embodiment 36. The method of any one of any one of the precedingembodiments, wherein the dilution factor for each of the first dilutionseries and the second dilutions series is at least a two-fold, athree-fold, a four-fold, a five-fold, a six-fold, a seven-fold, aneight-fold, a nine-fold, a ten-fold dilution.

Embodiment 37. The method of any one of embodiments 1 to 35, wherein thedilution factor for each of the first dilution series and the seconddilutions series is an exponential or logarithmic dilution factor.

Embodiment 38. The method of any one of the preceding embodiments,wherein the first biological sample and the second biological sample arethe same type of biological sample.

Embodiment 39. The method of any one of embodiments 1 to 35, wherein thefirst dilution series and the second dilution series are each at least a5-point serial titration at at least a 1:2 titration factor.

Embodiment 40. The method of any one of the preceding embodiments,wherein the method further comprises horizontally translating the levelof at least one analyte from a biological test sample to the compositedilution model for the at least one analyte, thereby determining therelative dilution of the biological test sample.

Embodiment 41. The method of embodiment 40, wherein the biological testsample and the first and second biological samples used to form thecomposite dilution model are the same sample type.

Embodiment 42. The method of embodiment 40 or embodiment 41, wherein thebiological test sample and the first and second biological samples usedto form the composite dilution model are urine samples or are derivedfrom urine samples.

Embodiment 43. A method for determining a relative dilution of abiological test sample from a subject, the method comprisinghorizontally translating the level of at least one analyte from abiological test sample to a composite dilution model developed for theat least one analyte, thereby determining the relative dilution of thebiological test sample from the subject.

Embodiment 44. The method of any one of embodiments 40 to 43, comprisinghorizontally translating the level of at least 2, at least 3, at least4, at least 5, at least 6, at least 7, at least 8, at least 9, at least10, at least 11, at least 12, at least 13, at least 14, at least 15, atleast 16, at least 17, at least 18, at least 19, at least 20, at least21, at least 22, at least 23, at least 24, at least 25, at least 26, atleast 27, at least 28, at least 29, at least 30, at least 50, at least75, at least 100, at least 150, or at least 200 different analytes fromthe biological test sample to the respective composite dilution modeldeveloped for each of the different analytes to determine the relativedilution of the biological test sample for each of the differentanalytes, and using the relative dilution for each of the differentanalytes to determine the relative dilution of the biological testsample.

Embodiment 45. The method of embodiment 44, wherein the relativedilution of the biological test sample is derived from the centraltendency of the relative dilutions of each of the different analytes.

Embodiment 46. The method of embodiment 44 or embodiment 45, wherein therelative dilution of the biological test sample is derived from themedian, mean or mode of the relative dilutions of each of the differentanalytes.

Embodiment 47. The method of any one of embodiments 43 to 46, whereinthe composite dilution model was developed using the method of any oneof embodiments 1 to 42.

Embodiment 48. The method of any one of embodiments 43 to 48, whereinthe biological test sample and the samples used to develop the compositedilution model are the same sample type.

Embodiment 49. The method of embodiment 41, wherein the biological testsample and the samples used to develop the composite dilution model areurine samples or derived from urine samples.

Embodiment 50. The method of any one of embodiments 40 to 49, furthercomprising calculating the relative dilution of the biological testsample with the derived relative dilution factor.

Embodiment 51. The method of any one of the preceding embodiments,wherein each analyte is a target protein.

Embodiment 52. A computer system, comprising:

-   -   a non-transitory memory storing instructions; and    -   one or more hardware processors coupled to the non-transitory        memory and configured to read the instructions from the        non-transitory memory to cause the computer system to perform        operations comprising:        -   receiving analyte measurement data from a plurality of            different biological samples;        -   analyzing respective analyte measurements for each one of a            plurality of selected analytes in the analyte measurement            data; and        -   generating a composite dilution model for each one of the            selected analytes based on the analyzing.

Embodiment 53. The computer system of embodiment 52, wherein theanalyzing comprises:

-   -   determining, for each selected analyte, the biological sample        with the greatest linear dilution range.

Embodiment 54. The computer system of embodiment 53, wherein theanalyzing further comprises:

-   -   generating, for each selected analyte, a reference dilution        model based on the determined greatest linear range for the        respective selected analyte.

Embodiment 55. The computer system of embodiment 54, wherein theanalyzing further comprises:

-   -   translating, for each selected analyte, the analyte measurement        data associated with the respective selected analyte based on        the reference dilution model generated for the respective        selected analyte.

Embodiment 56. The computer system of any one of embodiments 52 to 55,wherein the biological samples are urine samples.

Embodiment 57. The computer system of any one of embodiments 52 to 56,wherein the analyte measurement data comprises relative fluorescenceunit (RFU) measurements.

Embodiment 58. A non-transitory computer-readable medium comprisingcomputer-readable instructions which, when executed by a processingdevice, cause the processing device to perform operations comprising:

-   -   receiving analyte measurement data from a plurality of different        biological samples;    -   analyzing respective analyte measurements for each one of a        plurality of selected analytes in the analyte measurement data;        and    -   generating a composite dilution model for each one of the        selected analytes based on the analyzing.

Embodiment 59. A computer-implemented method for generating a compositedilution model for biological material, comprising:

-   -   receiving, by one or more processing devices, analyte        measurement data from a plurality of different biological        samples;    -   analyzing, by one or more of the processing devices, respective        analyte measurements for each one of a plurality of selected        analytes in the analyte measurement data; and    -   generating, by one or more of the processing devices, a        composite dilution model for each one of the selected analytes        based on the analyzing.

Embodiment 60. A computer system, comprising:

-   -   a non-transitory memory storing instructions; and    -   one or more hardware processors coupled to the non-transitory        memory and configured to read the instructions from the        non-transitory memory to cause the computer system to perform        operations comprising:    -   receiving analyte measurement data for analytes in a biological        sample;    -   selecting a plurality of the analytes for determining a        predicted relative dilution of the biological sample;    -   receiving a composite dilution model generated for each        respective one of the selected analytes;    -   determining, for each one of the selected analytes, a predicted        relative dilution value based on a corresponding composite        dilution model generated for the respective selected analyte;        and    -   determining the predicted relative dilution of the biological        sample based on the determined predicted relative dilution        values for the selected analytes.

The foregoing and other objects, features, and advantages of theinvention will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

DESCRIPTION OF THE FIGURES

FIG. 1 shows determination of a linear range by serial dilutions.RFU=relative fluorescence units.

FIGS. 2A-2E show analysis of cystatin C (CST3) levels and ephrin type-Breceptor 6 (EPHB6). FIG. 2A shows measurement of CST3 levels usingserial dilutions of urine samples. FIG. 2B shows generation of aregression line for CST3 using a weighted linear regression model. FIG.2C shows a 4 parameter logistic function (4PL) fit of data for CST3.FIG. 2D shows measurement of EPHB6 levels using serial dilutions ofurine samples. FIG. 2E shows generation of a regression line for EPHB6using a weighted linear regression model.

FIGS. 3A-3C show analysis of platelet derived growth factor D (PDGFD)levels. FIG. 3A shows measurement of PDGFD levels using serial dilutionsof urine samples. FIG. 3B shows generation of a regression line using aweighted linear regression model. FIG. 3C shows a 4PL fit of data forPDGFD.

FIGS. 4A-4C show analysis of retinoic acid receptor responder 2(RARRES2) levels. FIG. 4A shows measurement of RARRES2 levels usingserial dilutions of urine samples. FIG. 4B shows generation of aregression line using a weighted linear regression model. FIG. 4C showsa 4PL fit of data for RARRES2.

FIGS. 5A-5C show analysis of interleukin 1 receptor like 2 (IL1RL2)levels. FIG. 5A shows measurement of IL1RL2 levels using serialdilutions of urine samples. FIG. 5B shows generation of a regressionline using a weighted linear regression model. FIG. 5C shows a 4PL fitof data for IL1RL2.

FIGS. 6A-6C show analysis of coagulation factor XI (F11) levels. FIG. 6Ashows measurement of F11 levels using serial dilutions of urine samples.FIG. 6B shows generation of a regression line using a weighted linearregression model. FIG. 6C shows a 4PL fit of data for F11.

FIGS. 7A-7C show analysis of septin 11 (SEPT11) levels. FIG. 7A showsmeasurement of SEPT11 levels using serial dilutions of urine samples.FIG. 7B shows generation of a regression line using a weighted linearregression model. FIG. 7C shows a 4PL fit of data for SEPT11.

FIGS. 8A-8C show analysis of thymopoietin (TWO) levels. FIG. 8A showsmeasurement of TWO levels using serial dilutions of urine samples. FIG.8B shows generation of a regression line using a weighted linearregression model. FIG. 8C shows a 4PL fit of data for TWO.

FIGS. 9A-9C show analysis of shisa family member 3 (SHISA3) levels. FIG.9A shows measurement of SHISA3 levels using serial dilutions of urinesamples. FIG. 9B shows generation of a regression line using a weightedlinear regression model. FIG. 9C shows a 4PL fit of data for SHISA3.

FIGS. 10A-10B show results of normalization of analyte levels foriduronidase (IDUA, 10A) and neogenin 1 (NEO1, 10B).

FIG. 11 shows distributions of predicted relative dilutions for 4 serial1:2 dilutions of a single sample. The lines shown are, from left toright, 0.3125%, 0.625%, 1.25%, 2.5%, 5%, 10%, 20%, and 40%.

FIG. 12 shows the target protein levels before normalization for 15study participants.

FIG. 13 shows the predicted relative dilution of each sample calculatedfrom composite dilution curves, for 15 study participants.

FIG. 14 shows the normalized samples for the 15 study participants.

FIGS. 15A-15D show non-normalized (15A and 15C) and normalized (15B and15D) measurements of analytes from urine of a single study participant.

FIGS. 16A-16D show non-normalized (16A and 16C) and normalized (16B and16D) measurements of analytes from urine of a single study participant.

FIG. 17A-17B shows the F-statistic for non-normalized and normalizeddata for each study participant (17A) and fold reduction in F statisticfor each study participant (17B).

FIGS. 18A and 18B show a flow diagram (A) and graphs (B) demonstrating afirst exemplary method of forming a composite dilution model.

FIGS. 19A and 19B show a flow diagram (A) and graphs (B) demonstrating asecond exemplary method of forming a composite dilution model.

FIGS. 20A and 20B show a flow diagram (A) and graphs (B) demonstrating athird exemplary method of forming a composite dilution model.

FIGS. 21A and 21B show a flow diagram (A) and graphs (B) demonstrating afourth exemplary method of forming a composite dilution model.

FIGS. 22A and 22B show a flow diagram (A) and graphs (B) demonstrating afifth exemplary method of forming a composite dilution model.

FIGS. 23A and 23B show a flow diagram (A) and graphs (B) demonstrating asixth exemplary method of forming a composite dilution model.

FIGS. 24A and 24B show a flow diagram (A) and graphs (B) demonstrating aseventh exemplary method of forming a composite dilution model.

FIG. 25 is a block diagram illustrating an example system architecture,in accordance with various examples of the present disclosure.

FIG. 26 and FIG. 27 are exemplary flow diagrams illustrating generatinga composite dilution model for each of a plurality of analytes presentin different biological samples, according to examples of the presentdisclosure.

FIG. 28 is a flow diagram illustrating application of composite dilutionmodels to a new biological sample for predicting a relative dilution ofthe new sample, according to an example of the present disclosure.

FIG. 29 is a block diagram of an exemplary computer system that mayperform one or more of the operations described herein.

DETAILED DESCRIPTION I. Terms and Methods

While the invention will be described in conjunction with certainrepresentative embodiments, it will be understood that the invention isdefined by the claims, and is not limited to those embodiments.

One skilled in the art will recognize many methods and materials similaror equivalent to those described herein may be used in the practice ofthe present invention. The present invention is in no way limited to themethods and materials described.

Unless defined otherwise, technical and scientific terms used hereinhave the meaning commonly understood by one of ordinary skill in the artto which this invention belongs. Definitions of common terms inmolecular biology may be found in Benjamin Lewin, Genes V, published byOxford University Press, 1994 (ISBN 0-19-854287-9); Kendrew et al.(eds.), The Encyclopedia of Molecular Biology, published by BlackwellScience Ltd., 1994 (ISBN 0-632-02182-9); and Robert A. Meyers (ed.),Molecular Biology and Biotechnology: a Comprehensive Desk Reference,published by VCH Publishers, Inc., 1995 (ISBN 1-56081-569-8). Althoughany methods, devices, and materials similar or equivalent to thosedescribed herein can be used in the practice of the invention, certainmethods, devices, and materials are described herein.

All publications, published patent documents, and patent applicationscited herein are hereby incorporated by reference to the same extent asthough each individual publication, published patent document, or patentapplication was specifically and individually indicated as beingincorporated by reference.

As used in this application, including the appended claims, the singularforms “a,” “an,” and “the” include the plural, unless the contextclearly dictates otherwise, and may be used interchangeably with “atleast one” and “one or more.” Thus, reference to “an aptamer” includesmixtures of aptamers, reference to “a probe” includes mixtures ofprobes, and the like.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “contains,” “containing,” and any variations thereof, areintended to cover a non-exclusive inclusion, such that a process,method, product-by-process, or composition of matter that comprises,includes, or contains an element or list of elements may include otherelements not expressly listed.

It is further to be understood that all base sizes or amino acid sizes,and all molecular weight or molecular mass values, given for nucleicacids or polypeptides are approximate, and are provided for description.

Further, ranges provided herein are understood to be shorthand for allof the values within the range. For example, a range of 1 to 50 isunderstood to include any number, combination of numbers, or sub-rangefrom 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, as well asfractions thereof unless the context clearly dictates otherwise.

Any concentration range, percentage range, ratio range, or integer rangeis to be understood to include the value of any integer within therecited range and, when appropriate, fractions thereof (such as onetenth and one hundredth of an integer), unless otherwise indicated.Also, any number range recited herein relating to any physical feature,such as polymer subunits, size or thickness, are to be understood toinclude any integer within the recited range, unless otherwiseindicated.

As used herein, “about” or “consisting essentially of” mean±20% of theindicated range, value, or structure, unless otherwise indicated.

The use of the alternative (e.g., “or”) should be understood to meaneither one, both, or any combination thereof of the alternatives.

In order to facilitate review of the various embodiments of thedisclosure, the following explanations of specific terms are provided:

Antibody: The term “antibody” refers to full-length antibodies of anyspecies and fragments and derivatives of such antibodies that retain theability to bind to antigen, including Fab fragments, F(ab′)2 fragments,single chain antibodies, Fv fragments, and single chain Fv fragments.The term “antibody” also includes synthetically-derived antibodies, suchas phage display-derived antibodies and fragments, affybodies andnanobodies.

Aptamer: As used herein, an “aptamer” refers to a nucleic acid that hasa specific binding affinity for a target molecule, wherein the bindingof the aptamer to the target molecule does not comprise Watson-Crickbase pairing. It is recognized that affinity interactions are a matterof degree; however, in this context, the “specific binding affinity” ofan aptamer for its target means that the aptamer binds to its targetgenerally with a much higher degree of affinity than it binds to othercomponents in a test sample. An “aptamer” is a set of copies of one typeor species of nucleic acid molecule that has a particular nucleotidesequence. An aptamer can include any suitable number of nucleotides,including any number of chemically modified nucleotides. The plural“aptamers” refers to more than one such set of molecules. Differentaptamers can have either the same or different numbers of nucleotides.Aptamers can be DNA or RNA or chemically modified nucleic acids, and canbe single-stranded, double-stranded, or contain both single- anddouble-stranded regions, and can include higher ordered structures. Anaptamer can also comprise a photoreactive or chemically reactivefunctional group to allow it to be covalently linked to itscorresponding target. Any of the aptamer methods disclosed herein caninclude the use of two or more aptamers that specifically bind the sametarget molecule. As further described below, an aptamer may include atag. If an aptamer includes a tag, all copies of the aptamer need nothave the same tag. Moreover, if different aptamers each include a tag,each different aptamer can have either the same tag or a different tag.

Biological Sample or Biological Matrix: As used herein, “biologicalsample” and “biological matrix” refer to any material, solution, ormixture obtained from an organism. This includes blood (including wholeblood, leukocytes, peripheral blood mononuclear cells, plasma, andserum), sputum, breath, urine, semen, saliva, meningeal fluid, amnioticfluid, glandular fluid, lymph fluid, nipple aspirate, bronchialaspirate, synovial fluid, joint aspirate, cells, a cellular extract, andcerebrospinal fluid. This also includes experimentally separatedfractions of all of the preceding. The terms “biological sample” and“biological matrix” also include materials, solutions, or mixturescontaining homogenized solid material, such as from a stool sample, atissue sample, or a tissue biopsy, for example. The terms “biologicalsample” and “biological matrix” also include materials, solutions, ormixtures derived from a cell line, tissue culture, cell culture,bacterial culture, viral culture or cell free biological system (e.g.IVTT).

Level: As used herein, “target protein level,” “analyte level” and“level” refer to a measurement that is made using any analytical methodfor detecting the analyte (such as a target protein) in a biologicalsample and that indicates the presence, absence, absolute amount orconcentration, relative amount or concentration, titer, level,expression level, ratio of measured levels, or the like, of, for, orcorresponding to the analyte in the biological sample. The exact natureof the “level” depends on the specific design and components of theparticular analytical method employed to detect the analyte.

C-5 Modified Pyrimidine: As used herein, the term “C-5 modifiedpyrimidine” refers to a pyrimidine with a modification at the C-5position. Examples of a C-5 modified pyrimidine include those describedin U.S. Pat. Nos. 5,719,273; 5,945,527; 9,163,056; and Dellafiore etal., 2016, Front. Chem., 4:18. Examples of a C-5 modification includesubstitution of deoxyuridine at the C-5 position with a substituentindependently selected from: benzylcarboxyamide (alternativelybenzylaminocarbonyl) (Bn), naphthylmethylcarboxyamide (alternativelynaphthylmethylaminocarbonyl) (Nap), tryptaminocarboxyamide(alternatively tryptaminocarbonyl) (Trp), phenethylcarboxyamide(alternatively phenethylamino carbonyl) (Pe),thiophenylmethylcarboxyamide (alternativelythiophenylmethylaminocarbonyl) (Th) and isobutylcarboxyamide(alternatively isobutylaminocarbonyl) (iBu) as illustrated immediatelybelow.

Chemical modifications of a C-5 modified pyrimidine can also becombined, singly or in any combination, with 2′-position sugarmodifications, modifications at exocyclic amines, and substitution of4-thiouridine and the like.

Representative C-5 modified pyrimidines include:5-(N-benzylcarboxyamide)-2′-deoxyuridine (BndU),5-(N-benzylcarboxyamide)-2′-O-methyluridine,5-(N-benzylcarboxyamide)-2′-fluorouridine,5-(N-isobutylcarboxyamide)-2′-deoxyuridine (iBudU),5-(N-isobutylcarboxyamide)-2′-O-methyluridine,5-(N-phenethylcarboxyamide)-2′-deoxyuridine (PedU),5-(N-thiophenylmethylcarboxyamide)-2′-deoxyuridine (ThdU),5-(N-isobutylcarboxyamide)-2′-fluorouridine,5-(N-tryptaminocarboxyamide)-2′-deoxyuridine (TrpdU),5-(N-tryptaminocarboxyamide)-2′-O-methyluridine,5-(N-tryptaminocarboxyamide)-2′-fluorouridine,5-(N-[1-(3-trimethylamonium) propyl]carboxyamide)-2′-deoxyuridinechloride, 5-(N-naphthylmethylcarboxyamide)-2′-deoxyuridine (NapdU),5-(N-naphthylmethylcarboxyamide)-2′-O-methyluridine,5-(N-naphthylmethylcarboxyamide)-2′-fluorouridine or5-(N-[1-(2,3-dihydroxypropyl)]carboxyamide)-2′-deoxyuridine).

Nucleotides can be modified either before or after synthesis of anoligonucleotide. A sequence of nucleotides in an oligonucleotide may beinterrupted by one or more non-nucleotide components. A modifiedoligonucleotide may be further modified after polymerization, such as,for example, by conjugation with any suitable labeling component.

As used herein, the term “at least one pyrimidine,” when referring tomodifications of a nucleic acid, refers to one, several, or allpyrimidines in the nucleic acid, indicating that any or all occurrencesof any or all of C, T, or U in a nucleic acid may be modified or not.

Capture Reagent: As used herein, a “capture agent” or “capture reagent”refers to a molecule that is capable of binding specifically to ananalyte, such as a biomarker, protein and/or peptide. A “target proteincapture reagent” refers to a molecule that is capable of bindingspecifically to a target protein. Nonlimiting exemplary capture reagentsinclude aptamers, antibodies, adnectins, ankyrins, other antibodymimetics and other protein scaffolds, autoantibodies, chimeras, smallmolecules, nucleic acids, lectins, ligand-binding receptors, imprintedpolymers, avimers, peptidomimetics, hormone receptors, cytokinereceptors, synthetic receptors, and modifications and fragments of anyof the aforementioned capture reagents. In some embodiments, a capturereagent is selected from an aptamer and an antibody.

Control Level: A “control level” of a target molecule refers to thelevel of the target molecule in the same sample type from an individualthat does not have the disease or condition, or from an individual thatis not suspected or at risk of having the disease or condition, or froman individual that has a non-progressive form of the disease orcondition. Further, a “control level” may refer to a reference based onthe average or what is considered within normal or healthy parameters. A“control level” may also refer to a reference level taken at a previoustime and that is used to compare to a later measured or detected levelof a target. For example, the level of a target may be detected at timepoint A, and then detected at time point B, where time point B is aftertime point A. In a more specific example, time point A may be consideredtime zero (0) or day zero (0) and time point B may be minutes (e.g., 10,20, 30, 40, 50, 60 minutes after time point A), hours (e.g, 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or24 hours after time point A), days (e.g, 1, 2, 3, 4, 5, 6 or 7 daysafter time point A), weeks (e.g., 1, 2, 3 or 4 weeks after time pointA), months (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 months aftertime point A) and even years (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55 or 60 years after timepoint A) after time point A. A “control level” of a target molecule neednot be determined each time the present methods are carried out, and maybe a previously determined level that is used as a reference orthreshold to determine whether the level in a particular sample ishigher or lower than a normal level.

Correspondence correlation: “Correspondence correlation” or “concordancecorrelation coefficient” measures the agreement between two continuousvariables X and Y (e.g., predicted, estimated or determined, andactual). The “correspondence correlation” evaluates the degree to whichpairs fall on the 45° line, and contains measurements of accuracy andprecision (or the “Lin's Condordance”). Additional information may befound in Lin, Biometrics, Vol. 45, No. 1 (March, 1989), 255-268, whichis hereby incorporated by reference. Other methods for determiningcorrelation that may be used herein includes, but are not limited to,Pearson correlation coefficient, the paired t-test, least squaresanalysis of slope (=1) and intercept (=0), the coefficient of variationand the intraclass correlation coefficient. In certain embodiments, thecorrespondence correlation is determined by the method selected fromLin's Concordance, Pearson correlation coefficient, the paired t-test,least squares analysis of slope (=1) and intercept (=0), the coefficientof variation and the intraclass correlation coefficient.

Detecting: As used herein, “detecting” or “determining” with respect toan analyte level includes the use of both the instrument used to observeand record a signal corresponding to a analyte level and the material/srequired to generate that signal. In various embodiments, the level isdetected using any suitable method, including fluorescence,chemiluminescence, surface plasmon resonance, surface acoustic waves,mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomicforce microscopy, scanning tunneling microscopy, electrochemicaldetection methods, nuclear magnetic resonance, quantum dots, and thelike.

Diagnose: “Diagnose”, “diagnosing”, “diagnosis”, and variations thereofrefer to the detection, determination, or recognition of a health statusor condition of an individual on the basis of one or more signs,symptoms, data, or other information pertaining to that individual. Thehealth status of an individual can be diagnosed as healthy/normal (i.e.,a diagnosis of the absence of a disease or condition) or diagnosed asill/abnormal (i.e., a diagnosis of the presence, or an assessment of thecharacteristics, of a disease or condition). The terms “diagnose”,“diagnosing”, “diagnosis”, etc., encompass, with respect to a particulardisease or condition, the initial detection of the disease; thecharacterization or classification of the disease; the detection of theprogression, remission, or recurrence of the disease; and the detectionof disease response after the administration of a treatment or therapyto the individual.

Dilution: “Dilution”, “dilution series” and variations thereof encompassseveral different types of dilutions, including, but not limited to,step dilutions, serial dilutions and combinations thereof. By way ofexample for a step dilution, if the dilution factor is 1000 (1:1000dilution), the user may first perform a 1:10 dilution (dilution factorof 10) followed by a 1:100 dilution (dilution factor of 100) using 1part solute from the 1:10 dilution and 99 parts of diluent, thusresulting in a dilution factor of 1000 or 1:1000 dilution of the solute.A serial dilution includes a succession of step dilutions, each havingthe same dilution factor, where the diluted material from the previousstep is used to make the subsequent dilution. By way of example for aserial dilution, to make a 5-point 1:2 serial dilution, entails using 1part solute and combining with 1 part diluent to make the first dilution(1^(st) point of the 5-point) in the dilution series, followed by 1 partsolute from the first dilution and combining with 1 part diluent to makethe second dilution (2^(nd) point of the 5-point) of the serial dilutionseries, so on and so forth until you reach the fifth successive serialdilution.

Dilution Factor: “Dilution factor” refers to the ratio of the parts ofsolute to parts of diluent. For example a dilution factor of 2 means a1:2 dilution where there are 1 part solute and 1 part diluent for atotal of 2 parts; and a dilution factor of 10 means a 1:10 dilutionwhere there are 1 part solute and 9 parts diluent for a total of 10parts.

Evaluate: “Evaluate”, “evaluating”, “evaluation”, and variations thereofencompass both “diagnose” and “prognose” and encompass determinations orpredictions about the future course of a disease or condition in anindividual has the disease or condition, as well as determinations andpredictions about eh likelihood that a disease or condition will occurin an individual who has not previously been diagnosed with the diseaseor condition, as well as determinations or predictions regarding thelikelihood that a disease or condition will recur in an individual whois in remission or is believed to have been cured of the disease. Theterm “evaluate” also encompasses assessing an individual's response to atherapy, such as, for example, predicting whether an individual islikely to respond favorably to a therapeutic agent or is unlikely torespond to a therapeutic agent (or will experience toxic or otherundesirable side effects, for example), selecting a therapeutic agentfor administration to an individual, or monitoring or determining anindividual's response to a therapy that has been administered, or isbeing administered, to the individual.

Individual: As used herein, “individual” and “subject” are usedinterchangeably to refer to a test subject or patient. The individualcan be a mammal or a non-mammal. In various embodiments, the individualis a mammal. A mammalian individual can be a human or non-human. Invarious embodiments, the individual is a human. A healthy or normalindividual is an individual in which the disease or condition ofinterest is not detectable by conventional diagnostic methods.

Linear Regression: The term “linear regression”, as used herein, refersto an approach for modeling the relationship between a scalar dependentvariable y and one or more explanatory variables denoted x. The case ofone explanatory variable is called simple linear regression. For morethan one explanatory variable, it is called multiple linear regression.In general, linear regression may be used to fit a predictive model toan observed data set of y and x values. After developing such a model,if additional value of x is then given without its accompanying value ofy, the fitted model can be used to make a prediction of the value of y.

Marker: As used herein, “marker” and “biomarker” are usedinterchangeably to refer to a target molecule (or analyte) thatindicates or is a sign of a normal or abnormal process in an individualor of a disease or other condition in an individual. More specifically,a “marker” or “biomarker” is an anatomic, physiologic, biochemical, ormolecular parameter associated with the presence of a specificphysiological state or process, whether normal or abnormal, and, ifabnormal, whether chronic or acute. Biomarkers are detectable andmeasurable by a variety of methods including laboratory assays andmedical imaging. In some embodiments, a biomarker is a target protein.

Modified: As used herein, the terms “modify”, “modified”,“modification”, and any variations thereof, when used in reference to anoligonucleotide, means that at least one of the four constituentnucleotide bases (i.e., A, G, T/U, and C) of the oligonucleotide is ananalog or ester of a naturally occurring nucleotide. In someembodiments, the modified nucleotide confers nuclease resistance to theoligonucleotide. In some embodiments, the modified nucleotides lead topredominantly hydrophobic interactions of aptamers with protein targetsresulting in high binding efficiency and stable co-crystal complexes. Apyrimidine with a substitution at the C-5 position is an example of amodified nucleotide. Modifications can include backbone modifications,methylations, unusual base-pairing combinations such as the isobasesisocytidine and isoguanidine, and the like. Modifications can alsoinclude 3′ and 5′ modifications, such as capping. Other modificationscan include substitution of one or more of the naturally occurringnucleotides with an analog, internucleotide modifications such as, forexample, those with uncharged linkages (e.g., methyl phosphonates,phosphotriesters, phosphoamidates, carbamates, etc.) and those withcharged linkages (e.g., phosphorothioates, phosphorodithioates, etc.),those with intercalators (e.g., acridine, psoralen, etc.), thosecontaining chelators (e.g., metals, radioactive metals, boron, oxidativemetals, etc.), those containing alkylators, and those with modifiedlinkages (e.g., alpha anomeric nucleic acids, etc.). Further, any of thehydroxyl groups ordinarily present on the sugar of a nucleotide may bereplaced by a phosphonate group or a phosphate group; protected bystandard protecting groups; or activated to prepare additional linkagesto additional nucleotides or to a solid support. The 5′ and 3′ terminalOH groups can be phosphorylated or substituted with amines, organiccapping group moieties of from about 1 to about 20 carbon atoms,polyethylene glycol (PEG) polymers, in some embodiments, ranging fromabout 10 to about 80 kDa, PEG polymers, in some embodiments, rangingfrom about 20 to about 60 kDa, or other hydrophilic or hydrophobicbiological or synthetic polymers. In one embodiment, modifications areof the C-5 position of pyrimidines. These modifications can be producedthrough an amide linkage directly at the C-5 position or by other typesof linkages.

Polynucleotides can also contain analogous forms of ribose ordeoxyribose sugars that are generally known in the art, including2′-O-methyl-, 2′-O-allyl, 2′-fluoro- or 2′-azido-ribose, carbocyclicsugar analogs, α-anomeric sugars, epimeric sugars such as arabinose,xyloses or lyxoses, pyranose sugars, furanose sugars, sedoheptuloses,acyclic analogs and abasic nucleoside analogs such as methyl riboside.As noted above, one or more phosphodiester linkages may be replaced byalternative linking groups. These alternative linking groups includeembodiments wherein phosphate is replaced by P(O)S (“thioate”), P(S)S(“dithioate”), (O)NR₂ (“amidate”), P(O)R, P(O)OR′, CO or CH₂(“formacetal”), in which each R or R′ is independently H or substitutedor unsubstituted alkyl (1-20 C) optionally containing an ether (—O—)linkage, aryl, alkenyl, cycloalky, cycloalkenyl or araldyl. Not alllinkages in a polynucleotide need be identical. Substitution ofanalogous forms of sugars, purines, and pyrimidines can be advantageousin designing a final product, as can alternative backbone structureslike a polyamide backbone, for example.

Nucleic acid: As used herein, “nucleic acid,” “oligonucleotide,” and“polynucleotide” are used interchangeably to refer to a polymer ofnucleotides and include DNA, RNA, DNA/RNA hybrids and modifications ofthese kinds of nucleic acids, oligonucleotides and polynucleotides,wherein the attachment of various entities or moieties to the nucleotideunits at any position are included. The terms “polynucleotide,”“oligonucleotide,” and “nucleic acid” include double- or single-strandedmolecules as well as triple-helical molecules. Nucleic acid,oligonucleotide, and polynucleotide are broader terms than the termaptamer and, thus, the terms nucleic acid, oligonucleotide, andpolynucleotide include polymers of nucleotides that are aptamers but theterms nucleic acid, oligonucleotide, and polynucleotide are not limitedto aptamers.

Ordinary least squares: “Ordinary least squares” or “OLS” or “linearleast squares”, as used herein, refers to a method for estimating theunknown parameters in a linear regression model. This method minimizesthe sum of squared vertical distances between the observed responses inthe dataset and the responses predicted by the linear approximation. Theresulting estimator can be expressed by a simple formula, especially inthe case of a single regressor on the right-hand side.

Prognose: “Prognose”, “prognosing”, “prognosis”, and variations thereofrefer to the prediction of a future course of a disease or condition inan individual who has the disease or condition (e.g., predicting patientsurvival), and such terms encompass the evaluation of disease responseduring and/or after the administration of a treatment or therapy to theindividual.

SELEX: The terms “SELEX” and “SELEX process” are used interchangeablyherein to refer generally to a combination of (1) the selection ofaptamers that interact with a target molecule in a desirable manner, forexample binding with high affinity to a protein, with (2) theamplification of those selected nucleic acids. The SELEX process can beused to identify aptamers with high affinity to a specific analyte, suchas a target protein.

Sequence Identity: Sequence identity, as used herein, in the context oftwo or more nucleic acid sequences is a function of the number ofidentical nucleotide positions shared by the sequences (i.e., %identity=number of identical positions/total number of positions in thereference sequence×100), taking into account the number of gaps, and thelength of each gap that needs to be introduced to optimize alignment oftwo or more sequences. The comparison of sequences and determination ofpercent identity between two or more sequences can be accomplished usinga mathematical algorithm, such as BLAST and Gapped BLAST programs attheir default parameters (e.g., Altschul et al., J. Mol. Biol. 215:403,1990; see also BLASTN at www.ncbi.nlm.nih.gov/BLAST). For sequencecomparisons, typically one sequence acts as a reference sequence towhich test sequences are compared. When using a sequence comparisonalgorithm, test and reference sequences are input into a computer,subsequence coordinates are designated if necessary, and sequencealgorithm program parameters are designated. The sequence comparisonalgorithm then calculates the percent sequence identity for the testsequence(s) relative to the reference sequence, based on the designatedprogram parameters. Optimal alignment of sequences for comparison can beconducted, e.g., by the local homology algorithm of Smith and Waterman,Adv. Appl. Math., 2:482, 1981, by the homology alignment algorithm ofNeedleman and Wunsch, J. Mol. Biol., 48:443, 1970, by the search forsimilarity method of Pearson and Lipman, Proc. Nat'l. Acad. Sci. USA85:2444, 1988, by computerized implementations of these algorithms (GAP,BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package,Genetics Computer Group, 575 Science Dr., Madison, Wis.), or by visualinspection (see generally, Ausubel, F. M. et al., Current Protocols inMolecular Biology, pub. by Greene Publishing Assoc. andWiley-Interscience (1987)). As used herein, when describing the percentidentity of a nucleic acid, such as an aptamer, the sequence of which isat least, for example, about 95% identical to a reference nucleotidesequence, it is intended that the nucleic acid sequence is identical tothe reference sequence except that the nucleic acid sequence may includeup to five point mutations per each 100 nucleotides of the referencenucleic acid sequence. In other words, to obtain a desired nucleic acidsequence, the sequence of which is at least about 95% identical to areference nucleic acid sequence, up to 5% of the nucleotides in thereference sequence may be deleted or substituted with anothernucleotide, or some number of nucleotides up to 5% of the total numberof nucleotides in the reference sequence may be inserted into thereference sequence (referred to herein as an insertion). These mutationsof the reference sequence to generate the desired sequence may occur atthe 5′ or 3′ terminal positions of the reference nucleotide sequence oranywhere between those terminal positions, interspersed eitherindividually among nucleotides in the reference sequence or in one ormore contiguous groups within the reference sequence.

SOMAmer: The term SOMAmer or SOMAmer reagent, as used herein, refers toan aptamer having improved off-rate characteristics. SOMAmer reagentsare alternatively referred to as Slow Off-Rate Modified Aptamers, andmay be selected via the improved SELEX methods described in U.S.Publication No. 20090004667, entitled “Method for Generating Aptamerswith Improved Off-Rates”, which is incorporated by reference in itsentirety. In some embodiments, a slow off-rate aptamer (including anaptamers comprising at least one nucleotide with a hydrophobicmodification) has an off-rate (t½) of ≥2 minutes, ≥4 minutes, ≥5minutes, ≥8 minutes, ≥10 minutes, ≥15 minutes≥30 minutes, ≥60 minutes,≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or≥240 minutes.

Substantially Equivalent: The phrase “substantially equivalent”, as usedherein, denotes a sufficiently high degree of similarity between twonumeric values such that one of skill in the art would consider thedifference between the two values to be of little or no biologicaland/or statistical significance within the context of the characteristicmeasured by the values. More specifically, the difference between thetwo values (e.g., the difference between the reference value and theregression model reference value) is preferably less than about 25%, orless than about 20%, or less than about 15% or less than about 10% orless than about 5% or less than about 4% or less than about 3% or lessthan about 2.5% or less than about 2% or less than about 1%.

Target Molecule: “Target”, “target molecule”, and “analyte” are usedinterchangeably herein to refer to any molecule of interest that may bepresent in a sample. The term includes any minor variation of aparticular molecule, such as, in the case of a protein, for example,minor variations in amino acid sequence, disulfide bond formation,glycosylation, lipidation, acetylation, phosphorylation, or any othermanipulation or modification, such as conjugation with a labelingcomponent, which does not substantially alter the identity of themolecule. A “target molecule”, “target”, or “analyte” refers to a set ofcopies of one type or species of molecule or multi-molecular structure.“Target molecules”, “targets”, and “analytes” refer to more than onetype or species of molecule or multi-molecular structure. Exemplarytarget molecules include proteins, polypeptides, nucleic acids,carbohydrates, lipids, polysaccharides, glycoproteins, hormones,receptors, antigens, antibodies, affybodies, antibody mimics, viruses,pathogens, toxic substances, substrates, metabolites, transition stateanalogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors,cells, tissues, and any fragment or portion of any of the foregoing. Insome embodiments, a target molecule is a protein, in which case thetarget molecule may be referred to as a “target protein.”

The foregoing and other objects, features, and advantages of theinvention will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

Although methods and materials similar or equivalent to those describedherein can be used in the practice or testing of the present disclosure,suitable methods and materials are described below. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

Detection and Determination of Analytes and Analyte Levels

An analyte level for the analytes described herein can be detected usingany of a variety of known analytical methods. In one embodiment, ananalyte level is detected using a capture reagent. In variousembodiments, the capture reagent can be exposed to the analyte insolution or can be exposed to the analyte while the capture reagent isimmobilized on a solid support. In some embodiments, the capture reagentcontains a feature that is reactive with a secondary feature on a solidsupport. In these embodiments, the capture reagent can be exposed to theanalyte in solution, and then the feature on the capture reagent can beused in conjunction with the secondary feature on the solid support toimmobilize the analyte on the solid support. The capture reagent isselected based on the type of analysis to be conducted. Capture reagentsinclude, but are not limited to, aptamers, antibodies, adnectins,ankyrins, other antibody mimetics and other protein scaffolds, chimeras,small molecules, F(ab′)₂ fragments, single chain antibody fragments, Fvfragments, single chain Fv fragments, nucleic acids, lectins,ligand-binding receptors, affybodies, nanobodies, imprinted polymers,avimers, peptidomimetics, hormone receptors, cytokine receptors, andsynthetic receptors, including modifications and fragments of any ofthese.

In some embodiments, an analyte level is detected using ananalyte/capture reagent complex.

In some embodiments, the analyte level is derived from theanalyte/capture reagent complex and is detected indirectly, such as, forexample, as a result of a reaction that is subsequent to theanalyte/capture reagent interaction, but is dependent on the formationof the analyte/capture reagent complex.

In some embodiments, the analyte level is detected directly from theanalyte in a biological sample.

In some embodiments, analytes are detected using a multiplexed formatthat allows for the simultaneous detection of two or more analytes in abiological sample. In some embodiments of the multiplexed format,capture reagents are immobilized, directly or indirectly, covalently ornon-covalently, in discrete locations on a solid support. In someembodiments, a multiplexed format uses discrete solid supports whereeach solid support has a unique capture reagent associated with thatsolid support, such as, for example, quantum dots. In some embodiments,an individual device is used for the detection of each one of multipleanalytes to be detected in a biological sample. Individual devices canbe configured to permit each analyte in the biological sample to beprocessed simultaneously. For example, a microtiter plate can be usedsuch that each well in the plate is used to analyze one or more ofmultiple analytes to be detected in a biological sample.

In one or more of the embodiments described herein, a fluorescent tagcan be used to label a component of the analyte/capture reagent complexto enable the detection of the analyte level. In various embodiments,the fluorescent label can be conjugated to a capture reagent specific toany of the analytes described herein using known techniques, and thefluorescent label can then be used to detect the corresponding analytelevel. Suitable fluorescent labels include rare earth chelates,fluorescein and its derivatives, rhodamine and its derivatives, dansyl,allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red,and other such compounds.

In some embodiments, the fluorescent label is a fluorescent dyemolecule. In some embodiments, the fluorescent dye molecule includes atleast one substituted indolium ring system in which the substituent onthe 3-carbon of the indolium ring contains a chemically reactive groupor a conjugated substance. In some embodiments, the dye moleculeincludes an AlexaFluor molecule, such as, for example, AlexaFluor 488,AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. Insome embodiments, the dye molecule includes a first type and a secondtype of dye molecule, such as, e.g., two different AlexaFluor molecules.In some embodiments, the dye molecule includes a first type and a secondtype of dye molecule, and the two dye molecules have different emissionspectra.

Fluorescence can be measured with a variety of instrumentationcompatible with a wide range of assay formats. For example,spectrofluorimeters have been designed to analyze microtiter plates,microscope slides, printed arrays, cuvettes, etc. See Principles ofFluorescence Spectroscopy, by J. R. Lakowicz, Springer Science+BusinessMedia, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress &Current Applications; Philip E. Stanley and Larry J. Kricka editors,World Scientific Publishing Company, January 2002.

In one or more embodiments, a chemiluminescence tag can optionally beused to label a component of the analyte/capture complex to enable thedetection of an analyte level. Suitable chemiluminescent materialsinclude any of oxalyl chloride, Rodamin 6G, Ru(bipy)₃ ²⁺, TMAE(tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3-trihydroxibenzene),Lucigenin, peroxyoxalates, Aryl oxalates, Acridinium esters, dioxetanes,and others.

In some embodiments, the detection method includes an enzyme/substratecombination that generates a detectable signal that corresponds to theanalyte level. Generally, the enzyme catalyzes a chemical alteration ofthe chromogenic substrate which can be measured using varioustechniques, including spectrophotometry, fluorescence, andchemiluminescence. Suitable enzymes include, for example, luciferases,luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO),alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme,glucose oxidase, galactose oxidase, and glucose-6-phosphatedehydrogenase, uricase, xanthine oxidase, lactoperoxidase,microperoxidase, and the like.

In some embodiments, the detection method can be a combination offluorescence, chemiluminescence, radionuclide and/or enzyme/substratecombinations that generate a measurable signal. In some embodiments,multimodal signaling could have unique and advantageous characteristicsin analyte assay formats.

In some embodiments, the analyte levels for the analytes describedherein can be detected using any analytical methods including,singleplex aptamer assays, multiplexed aptamer assays, singleplex ormultiplexed immunoassays, mRNA expression profiling, miRNA expressionprofiling, mass spectrometric analysis, histological/cytologicalmethods, etc. and as discussed below.

Determination of Analyte Levels Using Aptamer-Based Assays

Assays directed to the detection and quantification of physiologicallysignificant molecules in biological samples and other samples areimportant tools in scientific research and in the health care field. Oneclass of such assays involves the use of a microarray that includes oneor more aptamers immobilized on a solid support. The aptamers are eachcapable of binding to a target molecule in a highly specific manner andwith very high affinity. See, e.g., U.S. Pat. No. 5,475,096 entitled“Nucleic Acid Ligands”; see also, e.g., U.S. Pat. Nos. 6,242,246,6,458,543, and 6,503,715, each of which is entitled “Nucleic Acid LigandDiagnostic Biochip”. Once the microarray is contacted with a sample, theaptamers bind to their respective target molecules present in the sampleand thereby enable a determination of a analyte level corresponding to aanalyte.

In one aspect, the aptamer may include up to about 100 nucleotides, upto about 95 nucleotides, up to about 90 nucleotides, up to about 85nucleotides, up to about 80 nucleotides, up to about 75 nucleotides, upto about 70 nucleotides, up to about 65 nucleotides, up to about 60nucleotides, up to about 55 nucleotides, up to about 50 nucleotides, upto about 45 nucleotides, up to about 40 nucleotides, up to about 35nucleotides, up to about 30 nucleotides, up to about 25 nucleotides, andup to about 20 nucleotides. In a related aspect, the aptamer may be fromabout 25 to about 100 nucleotides in length (or from about 25, 26, 27,28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99or 100 nucleotides in length) or from about 25 to 50 nucleotides inlength (or from about 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or 50 nucleotides inlength).

An aptamer can be identified using any known method, including the SELEXprocess. Once identified, an aptamer can be prepared or synthesized inaccordance with any known method, including chemical synthetic methodsand enzymatic synthetic methods. In some embodiments, an aptamercomprises at least one nucleotide with a hydrophobic modification, suchas a hydrophobic base modification, allowing for hydrophobic contactswith a target protein. Such hydrophobic contacts, in some embodiments,contribute to greater affinity and/or slower off-rate binding by theaptamer. In some embodiments, an aptamer comprises at least two, atleast three, at least four, at least five, at least six, at least seven,at least eight, at least nine, or at least 10 nucleotides withhydrophobic modifications, where each hydrophobic modification may bethe same or different from the others. In some embodiments, thehydrophobic base modification is a C-5 modified pyrimidine. Nonlimitingexemplary C-5 modified pyrimidines are described herein and/or are knownin the art.

In some assay formats, the aptamers are immobilized on the solid supportprior to being contacted with the sample. Under certain circumstances,however, immobilization of the aptamers prior to contact with the samplemay not provide an optimal assay. For example, in some instances,pre-immobilization of the aptamers may result in inefficient mixing ofthe aptamers with the target molecules on the surface of the solidsupport, perhaps leading to lengthy reaction times and, therefore,extended incubation periods to permit efficient binding of the aptamersto their target molecules. Further, when photoaptamers are employed inthe assay and depending upon the material utilized as a solid support,the solid support may tend to scatter or absorb the light used to effectthe formation of covalent bonds between the photoaptamers and theirtarget molecules. Moreover, depending upon the method employed,detection of target molecules bound to their aptamers can be subject toimprecision, since the surface of the solid support may also be exposedto and affected by any labeling agents that are used. Finally,immobilization of the aptamers on the solid support generally involvesan aptamer-preparation step (i.e., the immobilization) prior to exposureof the aptamers to the sample, and this preparation step may affect theactivity or functionality of the aptamers.

Aptamer assays or “aptamer based assay(s)” that permit an aptamer tocapture its target in solution and then employ separation steps that aredesigned to remove specific components of the aptamer-target mixtureprior to detection have also been described (see, e.g., U.S. PublicationNo. 2009/0042206, entitled “Multiplexed Analyses of Test Samples”). Thedescribed aptamer assay methods enable the detection and quantificationof a non-nucleic acid target (e.g., a protein target) in a test sampleby detecting and quantifying a nucleic acid (i.e., an aptamer). Thedescribed methods create a nucleic acid surrogate (i.e., the aptamer)for detecting and quantifying a non-nucleic acid target, thus allowingthe wide variety of nucleic acid technologies, including amplification,to be applied to a broader range of desired targets, including proteintargets.

Aptamers can be constructed to facilitate the separation of the assaycomponents from an aptamer analyte complex (or photoaptamer analytecovalent complex) and permit isolation of the aptamer for detectionand/or quantification. In one embodiment, these constructs can include acleavable or releasable element within the aptamer sequence. In otherembodiments, additional functionality can be introduced into theaptamer, for example, a labeled or detectable component, a spacercomponent, or a specific binding tag or immobilization element. Forexample, the aptamer can include a tag connected to the aptamer via acleavable moiety, a label, a spacer component separating the label, andthe cleavable moiety. In one embodiment, a cleavable element is aphotocleavable linker. The photocleavable linker can be attached to abiotin moiety and a spacer section, can include an NHS group forderivatization of amines, and can be used to introduce a biotin group toan aptamer, thereby allowing for the release of the aptamer later in anassay method.

Homogenous assays, which in some embodiments are carried out with allassay components in solution, may not require separation of sample andreagents prior to the detection of signal. These methods are rapid andeasy to use.

In some embodiments, a method for signal generation takes advantage ofanisotropy signal change due to the interaction of a fluorophore-labeledcapture reagent with its specific analyte target. When the labeledcapture reacts with its target, the increased molecular weight causesthe rotational motion of the fluorophore attached to the complex tobecome much slower changing the anisotropy value. By monitoring theanisotropy change, binding events may be used to quantitatively measurethe analytes in solutions. Other methods include fluorescencepolarization assays, molecular beacon methods, time resolvedfluorescence quenching, chemiluminescence, fluorescence resonance energytransfer, and the like.

An exemplary solution-based aptamer assay that can be used to detect aanalyte level in a biological sample includes the following: (a)preparing a mixture by contacting the biological sample with an aptamerthat includes a first tag and has a specific affinity for the analyte,wherein an aptamer affinity complex is formed when the analyte ispresent in the sample; (b) exposing the mixture to a first solid supportincluding a first capture element, and allowing the first tag toassociate with the first capture element; (c) removing any components ofthe mixture not associated with the first solid support; (d) attaching asecond tag to the analyte component of the aptamer affinity complex; (e)releasing the aptamer affinity complex from the first solid support; (f)exposing the released aptamer affinity complex to a second solid supportthat includes a second capture element and allowing the second tag toassociate with the second capture element; (g) removing anynon-complexed aptamer from the mixture by partitioning the non-complexedaptamer from the aptamer affinity complex; (h) eluting the aptamer fromthe solid support; and (i) detecting the analyte by detecting theaptamer component of the aptamer affinity complex. For example, proteinconcentration or levels in a sample may be expressed as relativefluorescence units (RFU), which may be a product of detecting theaptamer component of the aptamer affinity complex (e.g., aptamercomplexed to target protein create the aptamer affinity complex). Thatis, for an aptamer-based assay, the protein concentration or levelcorrelates with the RFU.

A nonlimiting exemplary method of detecting analytes in a biologicalsample using aptamers is described in Kraemer et al., PLoS One 6(10):e26332.

Determination of Analyte Levels Using Immunoassays

Immunoassay methods are based on the reaction of an antibody to itscorresponding target or analyte and can detect the analyte in a sampledepending on the specific assay format. To improve specificity andsensitivity of an assay method based on immuno-reactivity, monoclonalantibodies and fragments thereof are often used because of theirspecific epitope recognition. Polyclonal antibodies have also beensuccessfully used in various immunoassays because of their increasedaffinity for the target as compared to monoclonal antibodies.Immunoassays have been designed for use with a wide range of biologicalsample matrices. Immunoassay formats have been designed to providequalitative, semi-quantitative, and quantitative results.

Quantitative results are generated through the use of a standard curvecreated with known concentrations of the specific analyte to bedetected. The response or signal from an unknown sample is plotted ontothe standard curve, and a quantity or level corresponding to the targetin the unknown sample is established.

Numerous immunoassay formats have been designed. ELISA or EIA can bequantitative for the detection of an analyte. This method relies onattachment of a label to either the analyte or the antibody and thelabel component includes, either directly or indirectly, an enzyme.ELISA tests may be formatted for direct, indirect, competitive, orsandwich detection of the analyte. Other methods rely on labels such as,for example, radioisotopes (I¹²⁵) or fluorescence. Additional techniquesinclude, for example, agglutination, nephelometry, turbidimetry, Westernblot, immunoprecipitation, immunocytochemistry, immunohistochemistry,flow cytometry, Luminex assay, and others (see ImmunoAssay: A PracticalGuide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005edition).

Exemplary assay formats include enzyme-linked immunosorbent assay(ELISA), radioimmunoassay, fluorescent, chemiluminescence, andfluorescence resonance energy transfer (FRET) or time resolved-FRET(TR-FRET) immunoassays. Examples of procedures for detecting analytesinclude analyte immunoprecipitation followed by quantitative methodsthat allow size and peptide level discrimination, such as gelelectrophoresis, capillary electrophoresis, planarelectrochromatography, and the like.

Methods of detecting and/or for quantifying a detectable label or signalgenerating material depend on the nature of the label. The products ofreactions catalyzed by appropriate enzymes (where the detectable labelis an enzyme; see above) can be, without limitation, fluorescent,luminescent, or radioactive or they may absorb visible or ultravioletlight. Examples of detectors suitable for detecting such detectablelabels include, without limitation, x-ray film, radioactivity counters,scintillation counters, spectrophotometers, colorimeters, fluorometers,luminometers, and densitometers.

Any of the methods for detection can be performed in any format thatallows for any suitable preparation, processing, and analysis of thereactions. This can be, for example, in multi-well assay plates (e.g.,96 wells or 386 wells) or using any suitable array or microarray. Stocksolutions for various agents can be made manually or robotically, andall subsequent pipetting, diluting, mixing, distribution, washing,incubating, sample readout, data collection and analysis can be donerobotically using commercially available analysis software, robotics,and detection instrumentation capable of detecting a detectable label.

Determination of Analyte Levels Using Gene Expression Profiling

Measuring mRNA in a biological sample may, in some embodiments, be usedas a surrogate for detection of the level of the corresponding proteinin the biological sample. Thus, in some embodiments, an analyte oranalyte panel described herein can be detected by detecting theappropriate RNA.

In some embodiments, mRNA expression levels are measured by reversetranscription quantitative polymerase chain reaction (RT-PCR followedwith qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA maybe used in a qPCR assay to produce fluorescence as the DNA amplificationprocess progresses. By comparison to a standard curve, qPCR can producean absolute measurement such as number of copies of mRNA per cell.

Northern blots, microarrays, Invader assays, and RT-PCR combined withcapillary electrophoresis have all been used to measure expressionlevels of mRNA in a sample. See Gene Expression Profiling: Methods andProtocols, Richard A. Shimkets, editor, Humana Press, 2004.

Determination of Analyte Levels Using Mass Spectrometry Methods

A variety of configurations of mass spectrometers can be used to detectanalyte levels. Several types of mass spectrometers are available or canbe produced with various configurations. In general, a mass spectrometerhas the following major components: a sample inlet, an ion source, amass analyzer, a detector, a vacuum system, and instrument-controlsystem, and a data system. Difference in the sample inlet, ion source,and mass analyzer generally define the type of instrument and itscapabilities. For example, an inlet can be a capillary-column liquidchromatography source or can be a direct probe or stage such as used inmatrix-assisted laser desorption. Common ion sources are, for example,electrospray, including nanospray and microspray or matrix-assistedlaser desorption. Common mass analyzers include a quadrupole massfilter, ion trap mass analyzer and time-of-flight mass analyzer.Additional mass spectrometry methods are well known in the art (seeBurlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman,New York (2000)).

Protein analytes and analyte levels can be detected and measured by anyof the following: electrospray ionization mass spectrometry (ESI-MS),ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionizationtime-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laserdesorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS),desorption/ionization on silicon (DIOS), secondary ion mass spectrometry(SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight(TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressurechemical ionization mass spectrometry (APCI-MS), APCI-MS/MS,APCI-(MS)^(N), atmospheric pressure photoionization mass spectrometry(APPI-MS), APPI-MS/MS, and APPI-(MS)^(N), quadrupole mass spectrometry,Fourier transform mass spectrometry (FTMS), quantitative massspectrometry, and ion trap mass spectrometry.

Sample preparation strategies are used to label and enrich samplesbefore mass spectroscopic characterization of protein analytes anddetermination of analyte levels. Labeling methods include but are notlimited to isobaric tag for relative and absolute quantitation (iTRAQ)and stable isotope labeling with amino acids in cell culture (SILAC).Capture reagents used to selectively enrich samples for candidateanalyte proteins prior to mass spectroscopic analysis include but arenot limited to aptamers, antibodies, nucleic acid probes, chimeras,small molecules, an F(ab′)₂ fragment, a single chain antibody fragment,an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, aligand-binding receptor, affybodies, nanobodies, ankyrins, domainantibodies, alternative antibody scaffolds (e.g. diabodies etc)imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleicacids, threose nucleic acid, a hormone receptor, a cytokine receptor,and synthetic receptors, and modifications and fragments of these.

Kits

Any combination of the analytes described herein can be detected using asuitable kit, such as for use in performing the methods disclosedherein. Furthermore, any kit can contain one or more detectable labelsas described herein, such as a fluorescent moiety, etc.

Methods of Normalizing Analyte Measurements in Biological Matrices

Methods of normalizing analyte measurements in biological matrices areprovided herein.

In certain embodiments, methods of developing composite dilution modelsare provided. Composite dilution models may be used to determine therelative dilution of one or more biological samples, such as urine. See,e.g., FIG. 10. The relative dilution may then be used to normalizemultiple samples such that differences in sample concentration (such asmay occur through differences in subject hydration level) may beminimized, and thus differences in analyte levels due to factors otherthan sample concentration may be detected. Such factors other thansample concentration that may affect analyte levels include, but are notlimited to, increases and decreases in analyte expression, changes inanalyte stability, changes in the rate or amount of analyte secreted,etc. Such differences in analyte levels may, in some embodiments, beindicative of a disease or condition or the likelihood of developing adisease or condition.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIGS. 18A and 18B. Levels of an analyte aremeasured in a dilution series from a first biological sample and in adilution series from a second biological sample. The first and secondbiological samples may be different time points from the sameindividual, or biological samples from different individuals. Thedilution series comprises levels of the analyte at various dilutions ofthe sample. A model is generated based on the first dilution series. Areference value in the second dilution series is selected. In someembodiments, the selected reference value in the second dilution seriesis near the midpoint of the second dilution series, for example, in aportion of the series that appears to be substantially linear. Thereference value in the second dilution series is then translated by anamount, ΔX, to a point on the model. The remaining points (i.e., levelsof the analyte at various dilutions) of the second dilution series aretranslated by the same amount, ΔX, to form a lined compositetranslation. A composite dilution model is generated by fitting afunction to the lined composite translation series. This compositedilution model can be used to determine the relative dilution of thebiological test sample from the subject.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIGS. 19A and 19B. Levels of an analyte aremeasured in a dilution series from a first biological sample and in adilution series from a second biological sample. The first and secondbiological samples may be different time points from the sameindividual, or biological samples from different individuals. Thedilution series comprises levels of the analyte at various dilutions ofthe sample. A model is generated based on the first dilution series. Areference value in the model is selected. In some embodiments, the modelreference value is an analyte level at a specific dilution of the model.The reference value in the model is then translated by an amount, ΔX, toa point in the second dilution series. The remaining values of the modelare translated by the same amount, ΔX, to form a lined compositetranslation series. A composite dilution model is generated by fitting afunction to the lined composite translation series. This compositedilution model can be used to determine the relative dilution of thebiological test sample from the subject.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIGS. 20A and 20B. Levels of an analyte aremeasured in a dilution series from a first biological sample and in adilution series from a second biological sample. The first and secondbiological samples may be different time points from the sameindividual, or biological samples from different individuals. Thedilution series comprises levels of the analyte at various dilutions ofthe sample. A reference value in the second dilution series is selected.In some embodiments, the selected reference value in the second dilutionseries is near the midpoint of the second dilution series, for example,in a portion of the series that appears to be substantially linear. Thereference value in the second dilution series is then translated by anamount, ΔX, to a value in the first dilution series. The remainingpoints (i.e., levels of the analyte at various dilutions) of the seconddilution series are translated by the same amount, ΔX, to form a linedcomposite translation series. A composite dilution model is generated byfitting a function to the lined composite translation series. Thiscomposite dilution model can be used to determine the relative dilutionof the biological test sample from the subject.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIGS. 21A and 21B. Levels of an analyte aremeasured in a dilution series from a first biological sample and in adilution series from a second biological sample. The first and secondbiological samples may be different time points from the sameindividual, or biological samples from different individuals. A model isgenerated based on the first dilution series. An arbitrary referencevalue that is an analyte level at a dilution value is selected. In someembodiments, the arbitrary reference value is an analyte level at aspecific dilution not found in the second dilution series or the model.A point on the mathematic model is then translated by an amount, ΔX, tothe arbitrary reference value and a point on the second dilution seriesis translated by an amount, ΔY, to the arbitrary reference value. Theremaining points (i.e., levels of the analyte at various dilutions) ofthe second dilution series are translated by the same amount, ΔY, andthe remaining values of the model are translated by the same amount, ΔX,to form a lined composite translation. A composite dilution model isgenerated by fitting a function to the lined composite translationseries. This composite dilution model can be used to determine therelative dilution of the biological test sample from the subject.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIG. 22A. Levels of an analyte are measured ina dilution series from a first biological sample and in a dilutionseries from a second biological sample. The first and second biologicalsamples may be different time points from the same individual, orbiological samples from different individuals. The dilution series showlevels of the analyte at various dilutions of the sample. An arbitraryreference value that is an analyte level at a dilution value isselected. In some embodiments, the arbitrary reference value is ananalyte level at a specific dilution not found in the first dilutionseries or the second dilution series. A point on the first dilutionseries is then translated by an amount, ΔX, to the arbitrary referencevalue and a point on the second dilution series is translated by anamount, ΔY, to the arbitrary reference value. The remaining points(i.e., levels of the analyte at various dilutions) of the first dilutionseries are translated by the same amount, ΔX, and the remaining points(i.e., levels of the analyte at various dilutions) of the seconddilution series are translated by the same amount, ΔY, to form a linedcomposite translation. A composite dilution model is generated byfitting a function to the lined composite translation series. Thiscomposite dilution model can be used to determine the relative dilutionof the biological test sample from the subject.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIGS. 23A and 23B. Levels of an analyte aremeasured in a dilution series from a first biological sample and in adilution series from a second biological sample. The first and secondbiological samples may be different time points from the sameindividual, or biological samples from different individuals. Thedilution series show levels of the analyte at various dilutions of thesample. A first model is generated based on the levels of the analyte ofthe first dilution series, and a second model is generated based on thelevels of the analyte of the second dilution series. A value from thefirst model is then translated by an amount, ΔX, to a value in thesecond model. The remaining values of the first model are thentranslated by the same amount, ΔX, to form a lined compositetranslation. A composite dilution model is generated by fitting afunction to the lined composite translation series. This compositedilution model can be used to determine the relative dilution of thebiological test sample from the subject.

In certain embodiments, a composite dilution model is developed by theexemplary method shown in FIGS. 24A and 24B. Levels of an analyte aremeasured in a dilution series from a first biological sample and in adilution series from a second biological sample. The first and secondbiological samples may be different time points from the sameindividual, or biological samples from different individuals. Thedilution series show levels of the analyte at various dilutions of thesample. A first model is generated based on the levels of the analyte ofthe first dilution series, and a second model is generated based on thelevels of the analyte of the second dilution series. An arbitraryreference value that is an analyte level at a dilution value isselected. In some embodiments, the arbitrary reference value is ananalyte level at a specific dilution not found in the first dilutionseries or the second dilution series. A point on the first model is thentranslated by an amount, ΔX, to the arbitrary reference value and apoint on the second model is translated by an amount, ΔY, to thearbitrary reference value. The remaining values of the first model aretranslated by the same amount, ΔX, and the remaining values of thesecond model are translated by the same amount, ΔY, to form a linedcomposite translation. A composite dilution model is generated byfitting a function to the lined composite translation series. Thiscomposite dilution model can be used to determine the relative dilutionof the biological test sample from the subject.

Any suitable model may be used in the methods described herein,including the models generated from the levels of the analytes in thefirst and/or second dilutions series, and the models used to generatethe composite dilutions model. Exemplary models that may be used in themethods include, but are not limited to, linear regression models, LOESScurve fitting models, non-linear regression models, spline fit models,mixed effects regression models, fixed effects regression models,generalized linear models, matrix decomposition models, and/or fourparameter logistic regression (4PL) models, and the like. The modelsgenerated from the levels of the analyte in the first and/or seconddilution series may be the same or different. Similarly, the model usedto generate the composite dilution model may be the same or differentfrom one or more of the models generated from the levels of theanalytes. One of ordinary skill in the art can select suitable modelsaccording to the particular application, and many such models are knownin the art.

In some embodiments, following development of the composite dilutionmodel, or using a composite dilution model previously developed, therelative dilution of a biological test sample may be determined. In somesuch embodiments, the level of at least one analyte from the biologicaltest sample is horizontally translated to the composite dilution modeldeveloped for the at least one analyte. The relative dilution of thebiological test sample may thereby be determined. In some embodiments,composite dilution models may be developed using a set of analytes, suchas, for example, at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120,140, 160, 180, or 200 analytes. The relative dilution of a biologicaltest sample is then determined for a subset of, or each of the analytes.The relative dilution of the biological test sample is then determined,in some embodiments, using the average, mean, median, or the like, ofthe relative dilutions based on each of the analytes. In someembodiments, analytes are selected that are not expected to vary inlevel between individuals, such as, for example, between individualswith and without a disease or condition.

Computer Devices, Methods and Software

In one aspect, the system further comprises one or more devices forproviding input data to one or more processors. The system furthercomprises a memory for storing a data set of ranked data elements.

In another aspect, the device for providing input data comprises adetector for detecting a characteristic of the data element, e.g., suchas a mass spectrometer or gene chip reader.

The system additionally may comprise a database management system. Userrequests or queries can be formatted in an appropriate languageunderstood by the database management system that processes the query toextract the relevant information from the database of training sets.

The system may be connectable to a network to which a network server andone or more clients are connected. The network may be a local areanetwork (LAN) or a wide area network (WAN), as is known in the art.Preferably, the server includes the hardware necessary for runningcomputer program products (e.g., software) to access database data forprocessing user requests.

The system may include an operating system (e.g., UNIX® or Linux) forexecuting instructions from a database management system. In one aspect,the operating system can operate on a global communications network,such as the internet, and utilize a global communications network serverto connect to such a network.

The system may include one or more devices that comprise a graphicaldisplay interface comprising interface elements such as buttons, pulldown menus, scroll bars, fields for entering text, and the like as areroutinely found in graphical user interfaces known in the art. Requestsentered on a user interface can be transmitted to an application programin the system for formatting to search for relevant information in oneor more of the system databases. Requests or queries entered by a usermay be constructed in any suitable database language.

The graphical user interface may be generated by a graphical userinterface code as part of the operating system and can be used to inputdata and/or to display inputted data. The result of processed data canbe displayed in the interface, printed on a printer in communicationwith the system, saved in a memory device, and/or transmitted over thenetwork or can be provided in the form of the computer readable medium.

The system can be in communication with an input device for providingdata regarding data elements to the system (e.g., expression values). Inone aspect, the input device can include a gene expression profilingsystem including, e.g., a mass spectrometer, gene chip or array reader,and the like.

The computer system may be a stand-alone system or part of a network ofcomputers including a server and one or more databases.

Some embodiments described herein can be implemented so as to include acomputer program product. A computer program product may include acomputer readable medium having computer readable program code embodiedin the medium for causing an application program to execute on acomputer with a database.

As used herein, a “computer program product” refers to an organized setof instructions in the form of natural or programming languagestatements that are contained on a physical media of any nature (e.g.,written, electronic, magnetic, optical or otherwise) and that may beused with a computer or other automated data processing system. Suchprogramming language statements, when executed by a computer or dataprocessing system, cause the computer or data processing system to actin accordance with the particular content of the statements. Computerprogram products include without limitation: programs in source andobject code and/or test or data libraries embedded in a computerreadable medium. Furthermore, the computer program product that enablesa computer system or data processing equipment device to act inpre-selected ways may be provided in a number of forms, including, butnot limited to, original source code, assembly code, object code,machine language, encrypted or compressed versions of the foregoing andany and all equivalents.

While various embodiments have been described as methods or apparatuses,it should be understood that embodiments can be implemented through codecoupled with a computer, e.g., code resident on a computer or accessibleby the computer. For example, software and databases could be utilizedto implement many of the methods discussed above. Thus, in addition toembodiments accomplished by hardware, it is also noted that theseembodiments can be accomplished through the use of an article ofmanufacture comprised of a computer usable medium having a computerreadable program code embodied therein, which causes the enablement ofthe functions disclosed in this description. Therefore, it is desiredthat embodiments also be considered protected by this patent in theirprogram code means as well.

Furthermore, the embodiments may be embodied as code stored in acomputer-readable memory of virtually any kind including, withoutlimitation, RAM, ROM, magnetic media, optical media, or magneto-opticalmedia. Even more generally, the embodiments could be implemented insoftware, or in hardware, or any combination thereof including, but notlimited to, software running on a general purpose processor, microcode,programmable logic arrays (PLAs), or application-specific integratedcircuits (ASICs).

It is also envisioned that embodiments could be accomplished as computersignals embodied in a carrier wave, as well as signals (e.g., electricaland optical) propagated through a transmission medium. Thus, the varioustypes of information discussed above could be formatted in a structure,such as a data structure, and transmitted as an electrical signalthrough a transmission medium or stored on a computer readable medium.

FIG. 25 illustrates an example system architecture 2500 in whichexamples of the present disclosure can be implemented. Systemarchitecture 2500 includes diagnostic machine 2502, robot 2504, network2506, data store 2508, server machine 2510, web server 2520, applicationserver 2522, analyte variability control system 2530, biological sampleanalyzer module 2540, composite dilution model generator module 2550,relative dilution prediction module 2560, client device 2570, analytemeasurement data 2580, and composite dilution models 2590.

Diagnostic machine 2502 is diagnostic equipment that receives andanalyzes one or more biological samples. For example, various types ofbiological samples may include, but are not limited to, urine, blood,plasma, serum, cerebrospinal fluid, or generally any other type ofbiological fluid. Diagnostic machine 2502 may collect or generateanalyte measurement data 2580 based on analyzing a biological sample,and such data may be stored or transferred to one or more other internalor external machines for processing, analysis, or use. Further,diagnostic machine 2502 generally may refer to one or more pieces ofdiagnostic equipment involved in processing biological samples suchAffymetrix® or Illumina® microarray machines.

In an example, robot 2504 drives, handles, and/or delivers biologicalsamples within diagnostic machine 2502 or amongst a plurality ofdiagnostic machines 2502 as part of processing biological samples. Forexample, robot 2504 generally may include, but is not limited to aTECAN® robot 2502 or any other robotic machines that provide automatedor semi-automated processing of biological samples in a lab environment.

In an example, diagnostic machine 2502 communicates with one or moredata store(s) 2508 and one or more server machine(s) 2510 via one ormore network(s) 2506. Network 2506 generally may be a public network(e.g., the Internet), a private network (e.g., a local area network(LAN) or wide area network (WAN)), or a combination thereof. In anexample, network 2506 may include the Internet and/or one or moreintranets, landline networks, wireless networks, and/or otherappropriate types of communication networks. In one example, network2506 may comprise a wireless telecommunications network (e.g., cellularphone network) adapted to communicate with other communication networks,such as the Internet.

Data store 2508 is persistent storage that is capable of storing varioustypes of data, such as alphanumeric text, audio, video and/or imagecontent. In some examples data store 2508 may be a network-attached fileserver, while in other examples data store 2508 might be some other typeof persistent storage such as an object-oriented database, a relationaldatabase, and so forth.

In an example, diagnostic machine 2502 may store and access varioustypes of data including analyte measurement data 2580 over network 2506.Diagnostic machine 2502 also may store and access such data on one ormore local data store(s) 2508 associated with diagnostic machine 2502(not shown) and/or one or more data store(s) 2508 local to servermachine 2510 or one or more other computing systems (not shown). In anexample, analyte measurement data 2580 may include, but is not limitedto relative fluorescence unit measurements for each of a plurality ofanalytes or proteins associated with one or more biological samples.

Data store 2508 also may receive, store, and provide composite dilutionmodels 2590 for use in controlling inter-sample analyte variability incomplex biological matrices. Composite dilution models 2590 may begenerated from analyte measurement data 2580 or may be directly orindirectly provided from another source. Further, composite dilutionmodels 2590 may be used to determine a predictive relative dilution fora biological sample type, which then may be used to adjust the relativedilution or other aspects of any one or more different, additional, ornewly received biological samples.

Server machine 2510 generally may be specialized diagnostic hardware, arackmount server, a personal computer, a portable digital assistant, amobile phone, a laptop computer, a tablet computer, a netbook, a desktopcomputer, or any combination thereof. Server machine 2510 may includeweb server 2520, application server 2522, and script detection system2530. In some examples, each of web server 2520, application server 2522and/or script detection system 2530 may run on one or more differentserver machine(s) 2510.

Web server 2520 may serve text, audio, video, and image content fromserver machine 2510 and/or data store 2508 to one or more clientdevice(s) 2570. Web server 2520 also may provide web-based applicationservices and business logic to client device(s) 2570. Client device(s)2570 may locate, access, and consume various forms of content andservices from web server 2520 using applications, such as a web browser.Web server 2520 also may receive text such as existing and new analytemeasurement data 2580, audio, video and image content from clientdevice(s) 2570 that is saved in one or more data store(s) 2508 forpurposes that may include analyzing, transforming, processing,persisting, and distributing such content.

In an example, web server 2520 is coupled to one or more applicationsserver(s) 2522 that provide applications and services to clientdevice(s) 2570 directly or with the assistance of web server 2520. Forexample, web server 2520 may provide client device(s) 2570 with accessto one or more specialized software applications in the field ofbiotechnology. Such functionality also may be provided, for example, asone or more different web applications, standalone applications,computer systems, plugins, web browser extensions, and applicationprogramming interfaces (APIs). In some examples, plugins and extensionsmay be referred to, individually or collectively, as add-ons.

Client device 2570 may be a personal computer (PC), laptop, a mobilephone, a tablet computer, or generally any other computing device.Client device 2570 may run an operating system (OS) that manageshardware and software of the client device 2570. A browser (not shown)may run on client device 2570. The browser may be a web browser that canaccess services and/or content provided by server machine 2510, webserver 2520, application server 2522, data store 2508, etc. In addition,other types of computer programs and computer scripts also may run onclient device 2570. For example, client device 2570 may use applications(i.e., “apps”) to access such content or communicate with server machine2510 without visiting or otherwise utilizing web pages.

In an example, functions and features of server machine 2510 also may beperformed by client device 2570, in whole or in part. In addition, thefunctionality attributed to a particular component may be performed bydifferent or multiple components operating together. Server machine 2510also may be accessed as a service provided to other systems or devicesvia application programming interfaces, and thus is not limited to usein websites.

Server machine 2510 also includes analyte variability control system2530. Analyte variability control system 2530 generally refers tospecialized computer hardware and/or software for controllinginter-sample analyte variability in complex biological matrices. Forexample, analyte variability control system 2530 may receive and analyzebiological samples associated with different subjects, generatecomposite dilution models 2590 for each of a plurality of analytes inthe biological samples, determine a relative dilution prediction modelfor the biological sample type of the biological samples based on aplurality of the generated composite dilution models 2590, receive andanalyze new biological samples of the biological sample type, and adjustone or more aspects of the newly received biological samples based onthe determined relative dilution prediction model for the biologicalsample type.

Examples of services provided by analyte variability control system 2530are further described in the present disclosure, including “Example 1:Generation of Empirical Matrix-Specific Standard Curves”, “Example 2:Application of Empirical Matrix-Specific Standard Curves”, “Example 3:Pilot Study Design to Characterize and Normalize the Variation inAnalyte Signal Due to Hydration Status of a Human Subject”, the figuresassociated with the present disclosure, and in the following paragraphs.

In an example, analyte variability control system 2530 includesbiological sample analyzer module 2540, composite dilution modelgenerator module 2550, and relative dilution prediction module 2560. Inother examples, functionality associated with biological sample analyzermodule 2540, composite dilution model generator module 2550, andrelative dilution prediction module 2560 may be combined, divided, andorganized in various arrangements.

In an example, biological sample analyzer module 2540 generally mayreceive and analyze analyte measurement data 2580 associated withbiological samples from different subjects. Composite dilution modelgenerator module 2550 generally may perform one or more steps togenerate a composite dilution model 2590 for each analyte in a selectedgroup of analytes from the analyte measurement data 2580. Relativedilution prediction module 2560 generally then may determine apredictive relative dilution for the biological sample type of thebiological samples based on the selected composite dilution models 2590generated from the biological samples. Relative dilution predictionmodule 2560 generally then may adjust one or more aspects of analytemeasurement data 2580 associated with different and/or newly receivedbiological samples based on the predictive relative dilution determinedfrom the selected composite dilution models 2590.

FIG. 26 is a flow diagram illustrating generating a composite dilutionmodel for each of a plurality of analytes present in differentbiological samples, according to examples of the present disclosure.Example method 2600 may be performed by processing logic that maycomprise specialized hardware (circuitry, dedicated logic, programmablelogic, microcode, etc.), specialized software (such as instructions runon a general purpose computer system, a dedicated machine, or hardwareprocessing devices), firmware, or any combination thereof.

In general, support for example method 2600 is provided throughout thepresent disclosure including in association with the non-limitingexamples, “Example 1: Generation of Empirical Matrix-Specific StandardCurves”, “Example 2: Application of Empirical Matrix-Specific StandardCurves”, and “Example 3: Pilot Study Design to Characterize andNormalize the Variation in Analyte Signal Due to Hydration Status of aHuman Subject” as described above.

Method 2600 begins at block 2602 when analyte variability control system2530 receives analyte measurement data 2580 associated with differentbiological samples. In an example, biological sample analyzer module2540 of analyte variability control system 2530 receives analytemeasurement data 2580 from diagnostic machine 2502, data store 2508,server machine 2510, client device 2570, or any of one or more othercomputer systems or storage devices.

In an example, biological sample analyzer module 2540 receives analytemeasurement data 2580 generated from a diagnostic machine 2502. Forexample, diagnostic machine 2502 may receive biological samples providedfrom multiple different human subjects. Such biological samples mayinclude urine, blood, plasma, serum, cerebrospinal fluid, or generallyany other type of biological fluid.

In an example, diagnostic machine 2502 measures analytes or proteinsassociated with each of the biological samples from the differentsubjects to generate analyte measurement data 2580 measured in relativefluorescence units (RFUs) or any other suitable measurement unit(s).Thus, in some examples, analyte measurement data 2580 may includerespective relative fluorescence unit measurements for each of aplurality of proteins associated with one or more different biologicalsamples. Such analyte measurement data 2580 may be preserved in datastore 2508 or any other persistent storage and later provided to one ormore other computer systems, such as server machine 2510.

In an example, biological sample analyzer module 2540 generally maytransform, adjust, and/or process analyte measurement data 2580 (e.g.,raw RFU data) in any number of steps in preparation for furtherprocessing by analyte variability control system 2530. For example, rawor partially processed analyte measurement data 2580 may be cleansed,formatted, normalized, calibrated, or manipulated in any of one or moredifferent steps. In other examples, analyte measurement data 2580received by biological sample analyzer module may be pre-processed andready for analysis upon receipt without further pre-processing ormanipulation.

At block 2604, analyte variability control system 2530 analyzesrespective analyte measurements for each of a plurality of selectedanalytes in the analyte measurement data. In an example, biologicalsample analyzer module 2540 of analyte variability control system 2530analyzes each of a plurality of selected analytes in analyte measurementdata 2508. For example, biological sample analyzer module 2540 mayanalyze each analyte from a subset of available measured analytes from aplurality of different biological samples. In an example, biologicalsample analyzer module 2540 may analyze analyte measurement data 2580for each analyte across different biological samples individually or inparallel (e.g., with two or more analytes across the differentbiological samples being processed at the same time).

In an example, biological sample analyzer module 2540 analyzes eachanalyte in preparation for generating a composite dilution model 2590corresponding to each respective analyte. For example, biological sampleanalyzer module 2540 may analyze an analyte for a plurality ofbiological samples that have been serially diluted. In one example,biological sample analyzer module 2540 determines the sample with thegreatest linear dilution range for an analyte, then fits a weightedlinear regression model to the data in the linear dilution range. Theassociated regression line then may be used as the reference for whichother samples are registered.

At block 2606, analyte variability control system 2530 generates acomposite dilution model 2590 for each one of the selected analytesbased on the analyzing performed at block 2604. In an example, compositedilution model generator module 2550 of analyte variability controlsystem 2530 translates respective analyte measurement data 2508 for ananalyte based on a corresponding reference dilution model generated bybiological sample analyzer module 2540. In an example, compositedilution model generator module 2550 then fits a 4-parameter logisticfunction (4PL) to the registered data for the analyte.

In an example, composite dilution model generator module 2550 thengenerates a corresponding composite dilution model 2590 for the analytebased on the 4PL associated with the analyte. Composite dilution modelgenerator module 2550 further generates composite dilution models 2590for each remaining analyte in a selected subset of analytes found inanalyte measurement data 2508. The composite dilution models 2590generated for the selected subset of analytes then may be used to adjustaspects of additional, different, or newly received biological samplesfor controlling inter-sample analyte variability.

FIG. 27 is a flow diagram illustrating generating a composite dilutionmodel for each of a plurality of analytes present in differentbiological samples, according to examples of the present disclosure.Example method 2700 may be performed by processing logic that maycomprise specialized hardware (circuitry, dedicated logic, programmablelogic, microcode, etc.), specialized software (such as instructions runon a general purpose computer system, a dedicated machine, or hardwareprocessing devices), firmware, or any combination thereof.

In general, support for example method 2700 is provided throughout thepresent disclosure including in association with the non-limitingexamples, “Example 1: Generation of Empirical Matrix-Specific StandardCurves”, “Example 2: Application of Empirical Matrix-Specific StandardCurves”, and “Example 3: Pilot Study Design to Characterize andNormalize the Variation in Analyte Signal Due to Hydration Status of aHuman Subject” as described above.

Method 2700 begins at block 2702 when analyte variability control system2530 receives analyte measurement data 2580 associated with differentbiological samples of the same biological sample type. In an example,biological sample analyzer module 2540 of analyte variability controlsystem 2530 receives analyte measurement data 2580 from data store 2508.Generally, such analyte measurement data may include relativefluorescence unit (RFU) measurements for each one of multiple differentproteins detected in each of a plurality of different biological samplesof the same biological sample type that have been collected fromdifferent subjects.

At block 2704, analyte variability control system 2530 analyzesrespective analyte measurements for each of a plurality of selectedanalytes in the analyte measurement data 2580. In an example, biologicalsample analyzer module 2540 of analyte variability control system 2530analyzes each one of a plurality of selected analytes from analytemeasurement data 2508 received at block 2704. For example, a subset ofavailable analytes from analyte measurement data 2508 may be selectedfor generating corresponding composite dilution models 2590 for use incontrolling inter-sample analyte variability in complex biologicalmatrices.

At block 2706, analyte variability control system 2530 generates areference dilution model for each selected analyte. In an example,biological sample analyzer module 2540 generates a reference dilutionmodel for each selected analyte based on the analyzing performed atblock 2704. For example, in one non-limiting example, biological sampleanalyzer module 2540 may determine which sample from a plurality ofsamples associated with a respective analyte has the greatest lineardilution range. Biological sample analyzer module 2540 then may fit aweighted linear regression model to the data in the linear dilutionrange. In addition, biological sample analyzer module 2540 similarly maygenerate a weighted linear regression model for each of the otherselected analytes, which respectively will be used, at least in part, ingenerating corresponding composite dilution models 2590 associated witheach respective selected analyte. Further description and examples areprovided in the present disclosure, for example, at least innon-limiting example, “Example 1: Generation of EmpiricalMatrix-Specific Standard Curves”.

At block 2708, analyte variability control system 2530 translatesrespective analyte measurement data for each selected analyte based on acorresponding reference dilution model generated for each respectiveanalyte. In an example, composite dilution model generator module 2550of analyte variability control system 2530 translates respective analytemeasurement data 2508 for each one of the selected analytes based on acorresponding reference dilution model generated by biological sampleanalyzer module 2540. Further description and examples are provided inthe present disclosure, for example, at least in non-limiting example,“Example 1: Generation of Empirical Matrix-Specific Standard Curves”.

At block 2710, analyte variability control system 2530 generates acomposite dilution model 2590 for each one of the selected analytesbased on the translated analyte measurement data. In an example,composite dilution model generator module 2550 fits a 4-parameterlogistic function (4PL) to the translated data for each of the selectedanalytes. For example, composite dilution model generator module 2550generates a corresponding composite dilution model 2590 for each one ofthe selected analytes based on the 4PL. Thus, composite dilution modelgenerator module 2550 generates a collection or series of compositedilution models 2590 comprising a composite solution module for each oneof the selected analytes.

Further description and examples describing block 2710 and applying thegenerated composite dilution models 2590 to other biological samples aredescribed in the present disclosure, for example, at least innon-limiting examples, “Example 1: Generation of EmpiricalMatrix-Specific Standard Curves” and “Example 2: Application ofEmpirical Matrix-Specific Standard Curves”.

In some examples, composite dilution model generator module 2550 furthergenerates one or more reports comprising information and details aboutvarious aspects associated with processing analyte measurement data 2580and generating composite dilution models 2590. For example, suchgenerated reports may include description about various findingsincluding potential or actual data abnormalities in analyte measurementdata 2580, pre-processing performed on analyte measurement data 2580,analysis of analyte measurement data 2580, generation of compositedilution models 2590, and predictive relative dilution determinations.In some examples, analyte variability control system 2530 storesgenerated reports in data store 2508 and may provide the reports andcorresponding analyte measurement data 2580 and/or composite dilutionmodels 2590 to client device 2570.

FIG. 28 is a flow diagram illustrating application of composite dilutionmodels to a new biological sample for predicting a relative dilution ofthe new sample, according to an example of the present disclosure.Example method 2800 may be performed by processing logic that maycomprise specialized hardware (circuitry, dedicated logic, programmablelogic, microcode, etc.), specialized software (such as instructions runon a general purpose computer system, a dedicated machine, or hardwareprocessing devices), firmware, or any combination thereof.

In general, support for example method 2800 is provided throughout thepresent disclosure including in association with the non-limitingexamples, “Example 1: Generation of Empirical Matrix-Specific StandardCurves”, “Example 2: Application of Empirical Matrix-Specific StandardCurves”, and “Example 3: Pilot Study Design to Characterize andNormalize the Variation in Analyte Signal Due to Hydration Status of aHuman Subject” as described above.

Method 2800 begins at block 2802 when analyte variability control system2530 receives analyte measurement data for analytes in a biologicalsample. In an example, biological sample analyzer module 2540 of analytevariability control system 2530 may receive analyte measurement data2580 of one or more new biological samples for analysis. For example,new biological samples generally may describe analyte measurement data2580 of biological samples, not included or considered in the generationof composite dilution models 2590 for analytes of a biological sampletype (e.g., urine). As such, new biological samples differ frombiological samples used in generating composite dilution models and suchassociated analyte measurement data 2580 may be received before or aftergeneration of such models.

At block 2804, analyte variability control system 2530 selects aplurality of the analytes for determining a predictive relative dilutionof the biological sample. In an example, biological sample analyzermodule 2540 of analyte variability control system 2530 analyzes analytemeasurement data 2580 of a new biological sample. For example,biological sample analyzer module 2540 may select a subset of analytesin analyte measurement data 2580 based on user preference and/or one ormore thresholds associated with relative fluorescence unit (RFU)measurements of analytes in analyte measurement data 2580.

In an example, biological sample analyzer module 2540 may select a topnumber of analytes in a new biological sample based on a “goodness offit” as compared to corresponding composite dilution models for thebiological sample type. In another example, biological sample analyzermodule 2540 also may select a number of analytes that have the abilityto flatten a serial titration series of the same sample. Furtherdescription and examples are provided in the present disclosure, forexample, at least in non-limiting example, “Example 2: Application ofEmpirical Matrix-Specific Standard Curves”.

At block 2806, analyte variability control system 2530 receives acomposite dilution model 2590 for each one of the analytes that havebeen selected. In an example, relative dilution prediction module 2560receives composite dilution models 2590 for each one of a plurality ofanalytes selected for determining a predictive relative solution of abiological sample. For example, relative dilution prediction module 2560receives composite dilution models 2590 generated for the selectedanalytes. Such composite dilution models 2590 may be generated based onmethod 2600, method 2700, or other examples of the present disclosure.

At block 2808, analyte variability control system 2530 determines apredictive relative dilution value for each one of the selected analytesbased on a corresponding composite dilution model associated with eachrespective analyte. In an example, for each one of the selectedanalytes, relative dilution prediction module 2560 projects a relativefluorescence unit (RFU) measurement for a respective analyte onto agenerated composite dilution model corresponding to the respectiveanalyte, thus generating a predicted relative dilution for each one ofthe respective analytes.

For example, relative dilution prediction module 2560 may take a firstRFU measurement for a first analyte in a new biological sample, projectthe first RFU measurement for the first analyte onto a first compositedilution model generated for the first analyte, and determine apredictive relative dilution value for the first analyte of the newbiological sample based on the projection. Similarly, relative dilutionprediction module 2560 may take a second RFU measurement for a secondanalyte in the same new biological sample, project the second RFUmeasurement for the second analyte onto a second composite dilutionmodel generated for the second analyte, and determine a predictiverelative dilution value for the second analyte of the new biologicalsample based on that projection (and so on and so forth for each one ofthe other selected analytes). Further description and examples areprovided in the present disclosure, for example, at least innon-limiting example, “Example 2: Application of EmpiricalMatrix-Specific Standard Curves”.

At block 2810, analyte variability control system 2530 determines thepredicted relative dilution of the biological sample based on thepredictive relative dilution values determined for each one of theselected analytes. In an example, relative dilution prediction module2560 creates a distribution of the predictive relative dilution valuesgenerated at block 2808 for the selected analytes of a new biologicalsample. Relative dilution prediction module 2560 then may determine andselect which of those predictive relative dilution values to use whenadjusting aspects of the new biological sample. For example, relativedilution prediction module 2560 may discard one or more sets of thegenerated predictive relative dilution values to create a final set ofgenerated predictive relative dilution values for adjusting a newbiological sample.

In an example, relative dilution prediction module 2560 trims tails fromthe distribution of predictive relative dilution values and uses amiddle percentage of the remaining values for determining the predictiverelative dilution for the new biological sample. Relative dilutionprediction module 2560 then determines the predicted relative dilutionof the biological sample. For example, relative dilution predictionmodule 2560 may analyze the remaining predictive relative dilutionvalues and generate a predictive relative dilution for the newbiological sample.

In one example, relative dilution prediction module 2560 determines thepredictive relative dilution for the new biological sample based on themedian of remaining predictive relative dilution values. Relativedilution prediction module 2560 generally also may determine thepredictive relative dilution for the new biological sample based onusing a formula or other analysis of the remaining predictive relativedilution values. Further description and examples are provided in thepresent disclosure, for example, at least in non-limiting example,“Example 2: Application of Empirical Matrix-Specific Standard Curves”.

In an example, analyte variability control system 2530 adjusts one ormore aspects of the new biological sample based on the predictiverelative dilution determined by relative dilution prediction module2560. For example, analyte variability control system 2530 may normalizeor otherwise adjust analyte measurement data 2580 of the new biologicalsample based on the predictive relative dilution for the new biologicalsample determined by relative dilution prediction module 2560. Further,in some examples, analyte variability control system 2530 may generatean associated report describing the associated processing andadjustment. The adjusted analyte measurement data 2580 for the newbiological sample then may be further examined and analyzed in view ofthe adjustment.

FIG. 29 illustrates a diagram of a machine in the exemplary form of acomputer system 2900 within which a set of instructions, for causing themachine to perform any one or more of the methodologies discussedherein, may be executed. In an example, the machine may be connected(e.g., networked) to other machines in a LAN, an intranet, an extranet,or the Internet. The machine may operate in the capacity of a server ora client machine in client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Forexample, the machine may be a personal computer (PC), a tablet PC, aPersonal Digital Assistant (PDA), a cellular telephone, a wearablecomputing device, a web appliance, a server machine, a specializeddiagnostic lab machine, or any other machine capable of executing a setof instructions (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The exemplary computer system 2900 includes a processing device(processor) 2902, a main memory 2904 (e.g., read-only memory (ROM),flash memory, dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM), double data rate (DDR SDRAM), or DRAM (RDRAM), etc.), astatic memory 2906 (e.g., flash memory, static random access memory(SRAM), etc.), and a data storage device 2918, which communicate witheach other via a bus 2930.

Processor 2902 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 2902 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 2902 alsomay be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processor 2902 is configured to execute instructions2922 for performing the operations and steps discussed herein.

The computer system 2900 may further include a network interface device2908. The computer system 2900 also may include a video display unit2910 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)),an alphanumeric input device 2912 (e.g., a keyboard), a cursor controldevice 2914 (e.g., a mouse), and a signal generation device 2916 (e.g.,a speaker).

The data storage device 2918 may include a computer-readable storagemedium 2928 on which is stored one or more sets of instructions 2922(e.g., software) embodying any one or more of the methodologies orfunctions described herein. The instructions 2922 also may reside,completely or at least partially, within the main memory 2904 and/orwithin the processor 2902 during execution thereof by the computersystem 2900, the main memory 2904 and the processor 2902 alsoconstituting computer-readable storage media. The instructions 2922 mayfurther be transmitted or received over a network 2920 via the networkinterface device 2908.

In one example, the instructions 2922 include instructions for ananalyte variability control system (e.g., analyte variability controlsystem 2530 of FIG. 25) and/or a software library comprising methodsthat call an analyte variability control system. While thecomputer-readable storage medium 2928 (machine-readable storage medium)is shown in an example to be a single medium, the term“computer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium that is capable of storing, encoding orcarrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. Further, the term “computer-readable storage medium”shall accordingly be taken to include, but not be limited to,solid-state memories, optical media, and magnetic media.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description have been presented in termsof steps leading to one or more desired results. Generally, such stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It has proven convenientat times, for reasons of common usage, to refer to these signals asbits, values, elements, symbols, characters, terms, numbers, or thelike.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “computing”, “comparing”, “applying”, “creating”,“ranking,” “classifying,” or the like, refer to the actions andprocesses of a specialized computer system, or similar specializedelectronic computing device, that manipulates and transforms datarepresented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Certain examples of the present disclosure also relate to an apparatusfor performing the operations herein. This apparatus may be constructedfor the intended purposes, or it may comprise specialized computerhardware and/or specialized computer programs selectively installed,activated, or configured to perform the intended purposes. Such computerprograms may be stored in a computer readable storage medium, such as,but not limited to, any type of disk including floppy disks, opticaldisks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs),random access memories (RAMs), EPROMs, EEPROMs, magnetic or opticalcards, or any type of media suitable for storing electronicinstructions.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other examples will be apparentto those of skill in the art upon reading and understanding the abovedescription. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

EXAMPLES

The following examples are provided to illustrate certain particularfeatures and/or embodiments. These examples should not be construed tolimit the disclosure to the particular features or embodimentsdescribed.

Example 1: Generation of Empirical Matrix-Specific Standard Curves

This example provides the means by which an exemplary composite dilutionmodel was generated from a complex biological matrix, for example urine,using relative protein measurements, as assayed with a capture reagent,for example an aptamer.

Traditionally, proteins levels in urine are normalized tophysiological-based measurements (e.g., total urine volume, creatinineconcentration or albumin:creatinine ratio) or restricted mediannormalization, which identifies a subset of capture reagents (e.g.,aptamers) that exhibit dilution linearity (or scale with the proteincontent of the sample) and use this information for standard mediannormalization.

A new normalization approach was developed that creates a compositedilution curve for each analyte (measured protein) based on a titrationseries of multiple samples from a complex matrix such as urine. Manycomposite dilution curves can then be used to more accurately estimatethe overall dilution of protein levels. Protein measurements were madein urine using an aptamer based assay whereby protein levels wererepresented by relative amounts (RFU or relative fluorescent units).Urine was chosen as an example matrix due to the commonly observedinter-sample variability in protein levels that results, for example,from the hydration level of the individual at the time the urine iscollected. Hydration levels may confound the relationship between aprotein level as measured from urine, and the clinical evaluation of thesubject being tested. Thus, by generating a composite dilution model,which compensates for the inter-sample variability, consistent andclinically relevant information about the test subject may be derivedfrom the protein levels in urine. Further, once the composite dilutionmodel is generated for the matrix, the same composite dilution model maybe used to compensate for inter-sample variability in samples from manysubjects, and consistent and clinically relevant information may bederived from the analyte measurements for those subjects.

By way of background information, quantitative inputs to a function willbe denoted X while outputs will be denoted by the variable Y. If X or Yis a matrix, the individual components can be accessed by subscripts X.For example, the output value of the j^(th) sample for the i^(th)aptamer would be X_(ij). The next sample for the same aptamer would beX_(i(j+1)). Vectors of values will be denoted as upper case while scalarvalues will be lower case.

The protein levels from at least three (3) serial titrations from atleast two (2) samples of the biological matrix of interest wereanalyzed, with i analytes (aptamers) for j samples with k serialtitrations represented as dilutions X_(ijk) and corresponding RFU valuesY_(ijk). For each analyte (or protein), the linear dilution range isdefined, which in the case of an aptamer based assay is the range ofdilutions where the measured RFU values scale approximately linearlywith the dilution of the sample. Starting with the lowest dilution, theRFU measurement for each dilution is used to establish a nominal levelwhere subsequent dilutions can be compared. The percent recovery isdefined as the measured RFU value divided by the expected RFU valuegiven an n-fold dilution. Starting at the k^(th) dilution in a series,the percent recovery of the m^(th) subsequent dilution is defined as

${\%{Recovery}_{m}} = {100\left( \frac{Y_{k}}{Y_{k + m}/n^{m}} \right)}$

An acceptable dilution range has percent recoveries within 50% of thenominal value determined at the highest dilution in the linear range. Aminimum of three serial dilutions from a nominal value are required todefine a linear range. The linear range (5 data points from 5 serialdilutions) is shown in FIG. 1 as the data points between the verticaldashed lines (Note the scale is logarithmic for visualization purposes,not suggesting log-linearity).

Analyte cystatin C (CST3) was used as a specific example. Nineteen (19)urine samples were serially diluted and the analyte CST3 levels weremeasured and plotted (see FIG. 2A). Thus, for each analyte i (e.g.,CST3), identify the sample j (of the 19 samples) with the greatestlinear dilution range. Fit a weighted linear regression model to thedata in the linear dilution range with weights inversely proportional tothe RFU measurement Y_(ij). The linear model for the i^(th) analytewould be the following:

Ŷ _(i)(x)=β_(0i)+β_(1i) x

Analyte ephrin type-B receptor 6 (EPHB6) was used as another specificexample. Nineteen (19) urine samples were serially diluted and theanalyte EPHB6 levels were measured and plotted (see FIG. 2D). Thus, foreach analyte i (e.g., EPHB6), identify the sample j (of the 19 samples)with the greatest linear dilution range. Fit a weighted linearregression model to the data in the linear dilution range with weightsinversely proportional to the RFU measurement Y_(ij). The linear modelfor the i^(th) analyte would be the following:

Ŷ _(i)(x)=β_(0i)+β_(1i) x

This regression line (or regression model) is now the reference to whichall other curves will be registered (the black line in FIG. 2B for CST3and FIG. 2E for EPHB6).

For each analyte i, register the titration curves by mapping (orhorizontally translating) the RFU value of the center point of itslinear range y_(ci) (or the reference value) to the regression line(regression model) to determine its relative dilution to the reference{circumflex over (x)} (relative reference dilution). For the j^(th)sample of the i^(th) analyte the relative reference dilution is thefollowing:

${\overset{\hat{}}{x}}_{ij} = \frac{y_{ci} - \beta_{0i}}{\beta_{1i}}$

Horizontally translate each titration curve Y_(ij) for a given analyteby its relative dilution value {circumflex over (x)}_(ij) (or ΔX) togenerate the translated curve Ŷ_(ij) (composite dilution model), wherethe center point of its linear range falls onto the reference regressionline. Alternatively, another way to go about this would be to fit alinear model for each titration series and register the curves bysetting the intercepts to the same value (e.g. 1). This process alignsthe curves relative to the reference, as shown in the right hand plotabove.

Ŷ _(ij) =Y _(ij) +{circumflex over (x)} _(ij)

Fit a 4 parameter logistic function (4PL) to the registered data foreach analyte. The 4PL equation is comprised of parameters for the lowerasymptote L, the upper asymptote U, the inflection point k, and Hill'sslope b. The model is symmetric about the inflection point and is fitusing nonlinear least squares.

${F(x)} = {\frac{L - U}{1 + \left( {x/k} \right)^{b}} + U}$

This generates an empirical standard curve for each analyte in thematrix of interest and in the presence of all capture reagents (e.g.,aptamers) and their respective target proteins. This is represented inthe FIG. 2C for CST3. Each 4PL fit (composite dilution model) can beranked by its goodness-of-fit using the Akaike information criterion(AIC).

FIGS. 3A-3C through 9A-9C show similar analysis of platelet derivedgrowth factor D (PDGFD, FIGS. 3A-3C), retinoic acid receptor responder 2(RARRES2, FIGS. 4A-4C), interleukin 1 receptor like 2 (IL1RL2, FIGS.5A-5C), coagulation factor XI (F11, FIGS. 6A-6C), septin 11 (SEPT11,FIGS. 7A-7C), thymopoietin (TMPO, FIGS. 8A-8C), and shisa family member3 (SHISA3, FIGS. 9A-9C)

Example 2: Application of Empirical Matrix-Specific Standard Curves

This example provides a method used to normalize the level of an analytemeasured in an assay using the empirical matrix-specific standard curve(or composite dilution model or the 4PL curve) generated in Example 1.

In general, the method entails selecting a subset of i analytes (aptamerbased protein measurements) to use in the normalization computations.

Feature selection can be performed in many ways. Three examples are: 1)by selecting the top i analytes by 4PL goodness of fit, ranked by AIC;2) performing feature selection for i analytes which have the ability toflatten a serial titration series of the same sample and/or 3) selectinganalytes that have signal level above background (e.g., 2-fold, 3-fold,4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold or 10-fold level of abovebackground).

For example, in relation to option 2 above, you would expect a two-foldincrease in the normalization scale factor for every 1:2 dilution. Thenfor each sample to be normalized, find the predicted relative dilutionby inversely solving the 4PL equation for all i analytes in thenormalization subset. For the i^(th) analyte (of n total) of sample k,the estimated relative dilution {tilde over (x)}_(ij) is

${\overset{˜}{x}}_{ij} = \left( \frac{L_{i} - y_{ik}}{y_{ik} - U_{i}} \right)^{1/b_{i}}$

FIGS. 10A and 10B show the application of the general process for 2analytes (iduronidase [IDUA] and neogenin 1 [NEO 1]). The RFUmeasurements for the samples that were normalized were projected ontothe 4PL curve to generate two predicted relative dilutions.

This information can then be used to find the estimated relativedilution for that sample. In some embodiments, the median predictedrelative dilution of all i^(th) analytes is determined. This processresults in a distribution of n predicted relative dilutions for eachsample to be normalized. The median of this distribution is thepredicted relative dilution for that sample, and the 4PL normalizationscale factor for that sample is 1 over the predicted relative dilution.

In some embodiments, the median, mean, or a value derived from thecentral tendency of the estimated relative dilutions is selected. Theestimated relative dilution for each sample can then be used tonormalize that sample. In some embodiments, a scaling factor for eachsample is generated by dividing a reference value by the estimatedrelative dilution for that sample. This reference value can, in someembodiments, be 1 (making the scaling factor the inverse of theestimated relative dilution) or the median estimated relative dilutionover all the samples to be normalized.

FIG. 11 shows distributions of predicted relative dilutions for 4 serial1:2 dilutions of a single sample, the median value is observed to scaleperfectly with the dilution of the sample using the 200 analytes withthe best AIC.

Example 3: Pilot Study Design to Characterize and Normalize theVariation in Analyte Signal Due to Hydration Status of a Human Subject

This example provides an overview of a pilot study used to characterizeand validate a normalization scheme for the variation of analyte signalmeasurements in human subjects under controlled hydration conditions.

A gender balanced cohort of 16 study participants aged 26 to 57 years(mean age of 33 years) was recruited for the study. The informed consentfor this study fell under the IRB-approved SomaLogic BiorepositoryResearch Protocol, or WIRB #20150206. All participants signed theconsent form and completed a brief health questionnaire where metadatawas collected, including age, gender, height, weight and otherdemographic information. All metadata was kept anonymous within thestudy.

Collection Protocol: The clinical information obtained from a urinespecimen may be influenced by the collection method and handling. Thus,the collection procedure employed for the study was a “midstream cleancatch” in order to reduce the incidence of cellular and microbialcontamination. Participants were requested to void the first portion ofthe urine stream into the toilet, which flushes the urethra andsignificantly reduces the opportunities of contaminants to enter thestream.

The urine midstream was collected into a 90 ml preservative-free,pre-labeled sterile collection cup with a secure, leak-resistant lid.Samples collected at SomaLogic were placed in a pre-labeled biohazardbag and immediately placed in a laboratory refrigerator designated forbiological samples for later aliquoting and storage at −80° C.

In order to maintain sample conditions similar to those received fromfuture prospective subjects, no pretreatment or centrifugation wasperformed before aliquoting. The time of collection was recorded for allsamples. In addition to 4 to 6 assay aliquots, one 8 ml aliquot wascreated for urinalysis.

Samples were anonymized by assigning a unique alphanumeric identifier toeach study participant and all collection tubes and biohazard bags werelabeled with this identifier as well as the time and date of collection.All metadata and proteomic data was entered into the database using thisidentifier. This process created a barrier of anonymity that preventedmetadata or proteomic data from being associated with a participant. Themaster key tying the identifier to the participant was createdinternally, and was kept confidential and only select individuals hadaccess on a need to know basis.

Participants were asked to refrain from anti-inflammatory medicationsfor 48 hours pre-study and to stop drinking fluids at 10:00 PM the nightbefore the study. Urination was allowed throughout the night, ifnecessary, but the “first void” urination in the morning was collected.

The Study Protocol: All participants were asked to refrain from drinkingany liquids (water, coffee, etc.) or exercising in the morning. Thefirst void sample collected at home was immediately placed on ice untilarrival at SomaLogic, where it was placed in the refrigerator at 2 to 8°C.

Participants were fed a calorically-balanced breakfast and asked toconsume 1.5% of the participant's body weight in water over a 30 minuteperiod (“hydration challenge”). This amounts to ˜850 ml for a 125 lbindividual and ˜1120 ml for a 175 lb individual. Instead ofpre-specifying the amount of food for each participant, food intake wascontrolled by simply asking participants to eat a conservative amount.

From 9 AM until noon, a portion of each urination was collected, with afinal ‘exit’ urine sample collected at approximately 12:00 PM. No waterwas allowed between 9:00 AM and 12:00 PM. Two to five serial samplesfollowed the hydration challenge with the last sample occurring atapproximately 12 PM. In addition, 15 participants provided anuncontrolled sample at 12 PM on the following day. In total, 81 urinesamples were collected from the 15 participants.

Per the collection protocol, all samples were collected “mid-stream” andwere sent to a clinical lab for a complete urinalysis, includingspecific gravity, total protein, urea, total salts and microbial titers.Total urine volume was not recorded.

Measurement of Analytes from Collected Samples: Urine samples from thesubjects in the study were assayed using an aptamer based assay thatmeasured the levels of over a thousand proteins. For each studyparticipant, FIG. 12 shows the data for each aptamer beforenormalization (represented as a line across multiple time points)represented as log₁₀ change from the median RFU signal for that aptamer.A general bias exists in the non-normalized data due to hydration stateof the participant, where signals for the first morning void sample(most concentrated) are higher compared to the median. This signal thendiminishes due to the hydration challenge, and then increases since thestudy participants refrained from drinking water post-hydrationchallenge.

This data was used to demonstrate the efficacy of the composite dilutionmodel to compensate for the hydration level of the subject and extractconsistent and clinically relevant information about the test subject.The composite dilution model was applied to all serial samples for eachstudy participant. Boxplots of the predicted relative dilutions for allserial samples for each study participant are shown in FIG. 13. Thesevalues were calculated from the composite dilution curves for allaptamers. The normalization process then computes a scale factor foreach sample by shifting the median of each boxplot to 1. FIG. 14 showsthe resulting time series after normalization using the compositedilution model to compensate for the hydration level of the subject,which is represented as RFU change from the median level of eachaptamer.

The boxplots and cumulative distribution functions (CDFs) in FIGS. 15and 16 show the distribution of signal at each time point in twoseparate study individuals. FIGS. 15A/C and 16A/C show the bias insignal level before normalization and FIGS. 15B/D and FIGS. 16B/D showthe distributions of signal level after normalization. Each line in theupper plots represents an aptamer signal level across samples.

FIGS. 15B and 16B show the reduction in systematic bias/variancequantified the repeated measures ANOVA F statistic, which may beinterpreted as the ratio of within group difference to between groupdifferences. High F statistics result from large differences betweentime points, and low F statistics show that differences between timepoints resemble what is to be expected given random chance. TheF-statistic was computed for the non-normalized and normalized data foreach study participant and is shown in FIG. 17A. The normalizationprocedure resulted in a highly significant reduction in longitudinalvariance (p<1E-8, N=15). FIG. 17B shows fold reduction in F statisticfor each study participant, which had a median of 81.88 and a range of13.12-524.96.

1. A method for generating a composite dilution model, the methodcomprising: a) determining the level of an analyte in a first dilutionseries of a first biological sample comprising the analyte, wherein thefirst dilution series comprises at least three (3) different dilutions,and the level of the analyte is determined in each of the at least threedifferent dilutions; b) determining the levels of the analyte in asecond dilution series of a second biological sample comprising theanalyte, wherein the second dilution series comprises at least three (3)different dilutions, and the level of the analyte is determined in eachof the at least three different dilutions; c) generating a model basedon the levels of the analyte of the first dilution series; d) selectinga reference value, wherein the reference value is selected from: (i) asecond dilution series reference value that is an analyte level at aspecific dilution of the second dilution series; (ii) a model referencevalue that is an analyte level at a specific dilution of the model; and(iii) an arbitrary reference value that is an analyte level at aspecific dilution, wherein the analyte level at that specific dilutionis not found in the second dilution series or the model; e) performingat least one horizontal translation, wherein the at least one horizontaltranslation is selected from: (i) a horizontal translation (ΔX) of thesecond dilution series reference value to a model value, wherein themodel value is an analyte level at a specific dilution of the model,wherein the second dilution series reference value and the model valueare equivalent or substantially equivalent; (ii) a horizontaltranslation (ΔX) of the model reference value to a second dilutionseries value, wherein the second dilution series value is an analytelevel at a specific dilution of the second dilution series, wherein themodel reference value and the second dilution series value areequivalent or substantially equivalent; and (iii) horizontaltranslations of a second dilution series value (ΔX) and a model value(ΔY) to the arbitrary reference value, wherein the model value is ananalyte level at a specific dilution of the model, wherein the seconddilution series value is an analyte level at a specific dilution of thesecond dilution series, and wherein the second dilution series value,the model value and the arbitrary reference value are equivalent orsubstantially equivalent; f) performing the at least one horizontaltranslation of the remaining values in (i) the second dilution series,(ii) the model, or (iii) the second dilution series and the model,wherein the horizontal translations of the remaining values results in alined composite translation; and g) fitting a function to the linedcomposite translation series thereby forming a composite dilution model.2. The method of claim 1, wherein the method comprises: d) selecting asecond dilution series reference value that is an analyte level at aspecific dilution of the second dilution series; e) performing ahorizontal translation (ΔX) of the second dilution series referencevalue to a model value, wherein the model value is an analyte level at aspecific dilution of the model, wherein the second dilution seriesreference value and the model value are equivalent or substantiallyequivalent; and f) performing the horizontal translation of theremaining values in the second dilution series, wherein the horizontaltranslation of the remaining values results in a lined compositetranslation.
 3. The method of claim 1, wherein the method comprises: d)selecting a model reference value that is an analyte level at a specificdilution of the model; e) performing a horizontal translation (ΔX) ofthe model reference value to a second dilution series value, wherein thesecond dilution series value is an analyte level at a specific dilutionof the second dilution series, wherein the model reference value and thesecond dilution series value are equivalent or substantially equivalent;and f) performing the horizontal translation of the remaining values inthe model, wherein the horizontal translation of the remaining valuesresults in a lined composite translation.
 4. The method of claim 1,wherein the method comprises: d) selecting an arbitrary reference valuethat is an analyte level at a specific dilution, wherein the analytelevel at that specific dilution is not found in the second dilutionseries or the model; e) performing a horizontal translation of a seconddilution series value (ΔX) and a model value (ΔY) to the arbitraryreference value, wherein the model value is an analyte level at aspecific dilution of the model, wherein the second dilution series valueis an analyte level at a specific dilution of the second dilutionseries, and wherein the second dilution series value, the model valueand the arbitrary reference value are equivalent or substantiallyequivalent; and f) performing the horizontal translations of theremaining values in the second dilution series and the model, whereinthe horizontal translations of the remaining values results in a linedcomposite translation.
 5. A method for generating a composite dilutionmodel, the method comprising: a) determining the level of an analyte ina first dilution series of a first biological sample comprising theanalyte, wherein the first dilution series comprises at least three (3)different dilutions, and the level of the analyte is determined in eachof the at least three different dilutions; b) determining the levels ofthe analyte in a second dilution series of a second biological samplecomprising the analyte, wherein the second dilution series comprises atleast three (3) different dilutions, and the level of the analyte isdetermined in each of the at least three different dilutions; c)generating a model based on the levels of the analyte of the firstdilution series; d) performing at least one horizontal translation,wherein the at least one horizontal translation is selected from: (i) ahorizontal translation (ΔX) of the second dilution series to the model;(ii) a horizontal translation (ΔX) of the model to the second dilutionseries; and (iii) horizontal translations of the second dilution series(ΔX) and the model (ΔY) to an arbitrary reference value, wherein thearbitrary reference value is an analyte level at a specific dilution,wherein the analyte level at that specific dilution is not found in thesecond dilution series or the model, wherein the at least one horizontaltranslation results in a lined composite translation series; and e)fitting a function to the lined composite translation series therebyforming a composite dilution model.
 6. The method of claim 5, whereinthe method comprises performing a horizontal translation (ΔX) of thesecond dilution series to the model.
 7. The method of claim 5, whereinthe method comprises performing a horizontal translation (ΔX) of themodel to the second dilution series.
 8. The method of claim 5, whereinthe method comprises performing a horizontal translation of the seconddilution series (ΔX) and the model (ΔY) to an arbitrary reference valuethat is an analyte level at a specific dilution, wherein the analytelevel at that specific dilution is not found in the second dilutionseries or the model.
 9. A method for generating a composite dilutionmodel, the method comprising: a) determining the level of an analyte ina first dilution series of a first biological sample comprising theanalyte, wherein the first dilution series comprises at least three (3)different dilutions, and the level of the analyte is determined in eachof the at least three different dilutions; b) determining the levels ofthe analyte in a second dilution series of a second biological samplecomprising the analyte, wherein the second dilution series comprises atleast three (3) different dilutions, and the level of the analyte isdetermined in each of the at least three different dilutions; c)selecting a reference value, wherein the reference value is selectedfrom: (i) a second dilution series reference value that is an analytelevel at a specific dilution of the second dilution series; and (ii) anarbitrary reference value that is an analyte level at a specificdilution, wherein the analyte level at that specific dilution is notfound in the first dilution series or the second dilution series; d)performing at least one horizontal translation, wherein the at least onehorizontal translation is selected from: (i) a horizontal translation(ΔX) of the second dilution series reference value to a first dilutionseries value, wherein the first dilution series value is an analytelevel at a specific dilution of the first dilution series, wherein thesecond dilution series reference value and the first dilution seriesvalue are equivalent or substantially equivalent; and (ii) horizontaltranslations of the first dilution series (ΔX) and the second dilutionseries (ΔY) to the arbitrary reference value; wherein the at least onehorizontal translation results in a lined composite translation series;and e) fitting a function to the lined composite translation seriesthereby forming a composite dilution model.
 10. The method of claim 9,wherein the method comprises performing a horizontal translation (ΔX) ofthe second dilution series to the first dilution series.
 11. The methodof claim 9, wherein the method comprises horizontal translations of thefirst dilution series (ΔX) and the second dilution series (ΔY) to anarbitrary reference value, wherein the arbitrary reference value is ananalyte level at a specific dilution, wherein the analyte level at thatspecific dilution is not found in the first dilution series or thesecond dilution series.
 12. A method for generating a composite dilutionmodel, the method comprising: a) determining the level of an analyte ina first dilution series of a first biological sample comprising theanalyte, wherein the first dilution series comprises at least three (3)different dilutions, and the level of the analyte is determined in eachof the at least three different dilutions; b) determining the levels ofthe analyte in a second dilution series of a second biological samplecomprising the analyte, wherein the second dilution series comprises atleast three (3) different dilutions, and the level of the analyte isdetermined in each of the at least three different dilutions; c)performing at least one horizontal translation, wherein the at least onehorizontal translation is selected from: (i) a horizontal translation(ΔX) of the second dilution series to the first dilution series; and(ii) horizontal translations of the first dilution series (ΔX) and thesecond dilution series (ΔY) to an arbitrary reference value, wherein thearbitrary reference value is an analyte level at a specific dilution,wherein the analyte level at that specific dilution is not found in thefirst dilution series or the second dilution series; wherein the atleast one horizontal translation results in a lined compositetranslation series; and d) fitting a function to the lined compositetranslation series thereby forming a composite dilution model.
 13. Themethod of claim 12, wherein the method comprises performing a horizontaltranslation (ΔX) of the second dilution series to the first dilutionseries.
 14. The method of claim 12, wherein the method compriseshorizontal translations of the first dilution series (ΔX) and the seconddilution series (ΔY) to an arbitrary reference value, wherein thearbitrary reference value is an analyte level at a specific dilution,wherein the analyte level at that specific dilution is not found in thefirst dilution series or the second dilution series.
 15. A method forgenerating a composite dilution model, the method comprising: a)determining the level of an analyte in a first dilution series of afirst biological sample comprising the analyte, wherein the firstdilution series comprises at least three (3) different dilutions, andthe level of the analyte is determined in each of the at least threedifferent dilutions; b) determining the levels of the analyte in asecond dilution series of a second biological sample comprising theanalyte, wherein the second dilution series comprises at least three (3)different dilutions, and the level of the analyte is determined in eachof the at least three different dilutions; c) generating a first modelbased on the levels of the analyte of the first dilution series and asecond model based on the levels of the analyte of the second dilutionseries; d) selecting a reference value, wherein the reference value isselected from: (i) a first model reference value that is an analytelevel at a specific dilution of the first model; and (ii) an arbitraryreference value that is an analyte level at a specific dilution, whereinthe analyte level at that specific dilution is not found in the firstmodel or the second model; d) performing at least one horizontaltranslation, wherein the at least one horizontal translation is selectedfrom: (i) a horizontal translation (ΔX) of the first model to the secondmodel; and (ii) horizontal translations of the first model (ΔX) and thesecond model (ΔY) to the arbitrary reference value; and wherein thehorizontal translation result in a lined composite translation series;and e) fitting a function to the lined composite translation seriesthereby forming a composite dilution model.
 16. The method of claim 15,wherein the method comprises performing a horizontal translation (ΔX) ofthe first model to the second model.
 17. The method of claim 15, whereinthe method comprises horizontal translations of the first model (ΔX) andthe second model (ΔY) to the arbitrary reference value.
 18. A method forgenerating a composite dilution model, the method comprising: a)determining the level of an analyte in a first dilution series of afirst biological sample comprising the analyte, wherein the firstdilution series comprises at least three (3) different dilutions, andthe level of the analyte is determined in each of the at least threedifferent dilutions; b) determining the levels of the analyte in asecond dilution series of a second biological sample comprising theanalyte, wherein the second dilution series comprises at least three (3)different dilutions, and the level of the analyte is determined in eachof the at least three different dilutions; c) generating a first modelbased on the levels of the analyte of the first dilution series and asecond model based on the levels of the analyte of the second dilutionseries; d) performing at least one horizontal translation, wherein theat least one horizontal translation is selected from: (i) a horizontaltranslation (ΔX) of the first model to the second model; and (ii)horizontal translations of the first model (ΔX) and the second model(ΔY) to an arbitrary reference value, wherein the analyte level at thatspecific dilution is not found in the first model or the second model;and wherein the horizontal translation result in a lined compositetranslation series; and e) fitting a function to the lined compositetranslation series thereby forming a composite dilution model.
 19. Themethod of claim 18, wherein the method comprises performing a horizontaltranslation (ΔX) of the first model to the second model.
 20. The methodof claim 18, wherein the method comprises horizontal translations of thefirst model (ΔX) and the second model (ΔY) to an arbitrary referencevalue.
 21. The method of any one of the preceding claims, wherein thelevel of the analyte is determined in each of at least 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15 or 16 different dilutions in the first dilutionseries.
 22. The method of any one of the preceding claims, wherein thelevel of the analyte is determined in each of at least 8 differentdilutions.
 23. The method of any one of the preceding claims, whereinthe level of the analyte is determined in each of at least 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15 or 16 different dilutions in the seconddilution series.
 24. The method of any one of the preceding claims,wherein the level of the analyte is determined in each of at least 8different dilutions in the second dilution series.
 25. The method of anyone of the preceding claims, wherein each model is independentlyselected from a linear regression model, a LOESS curve fitting model, anon-linear regression model, a spline fit model, a mixed effectsregression model, a fixed effects regression model, a generalized linearmodel, a matrix decomposition model, and a four parameter logisticregression (4PL) model.
 26. The method of any one of the previousclaims, wherein the level of the analyte is the relative amount of theanalyte or the analyte concentration.
 27. The method of any one of thepreceding claims, wherein the selected reference value is within thelinear range of the dilution series or model.
 28. The method of claim21, wherein the selected reference value is the center point of thelinear range.
 29. The method of any one of the preceding claims, whereinthe first and second biological samples comprise urine or are derivedfrom urine.
 30. The method of any one of the preceding claims, whereinthe first and second biological samples are collected from the samesubject.
 31. The method of any one of the preceding claims, wherein thefirst and second biological samples are collected from differentsubjects.
 32. The method of claim 31, wherein the first biologicalsample is collected at a first time point and the second biologicalsample is collected at a second time point.
 33. The method of claim 32,wherein the first time point and the second time point differ by atleast about 0.5 hours, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 13hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20hours, 21 hours, 22 hours, 23 hours, 24 hours, 36 hours, 48 hours, 60hours or 72 hours.
 34. The method of any one of the preceding claims,wherein the level of the analyte is measured by an assay the uses anaptamer, antibody, mass spectrophotometer or a combination thereof. 35.The method of any one of preceding claims, wherein the dilution factorfor each of the first dilution series and second dilution series is aconstant dilution factor.
 36. The method of any one of any one of thepreceding claims, wherein the dilution factor for each of the firstdilution series and the second dilutions series is at least a two-fold,a three-fold, a four-fold, a five-fold, a six-fold, a seven-fold, aneight-fold, a nine-fold, a ten-fold dilution.
 37. The method of any oneof claims 1 to 35, wherein the dilution factor for each of the firstdilution series and the second dilutions series is an exponential orlogarithmic dilution factor.
 38. The method of any one of the precedingclaims, wherein the first biological sample and the second biologicalsample are the same type of biological sample.
 39. The method of any oneof claims 1 to 35, wherein the first dilution series and the seconddilution series are each at least a 5-point serial titration at at leasta 1:2 titration factor.
 40. The method of any one of the precedingclaims, wherein the method further comprises horizontally translatingthe level of at least one analyte from a biological test sample to thecomposite dilution model for the at least one analyte, therebydetermining the relative dilution of the biological test sample.
 41. Themethod of claim 40, wherein the biological test sample and the first andsecond biological samples used to form the composite dilution model arethe same sample type.
 42. The method of claim 40 or claim 41, whereinthe biological test sample and the first and second biological samplesused to form the composite dilution model are urine samples or arederived from urine samples.
 43. A method for determining a relativedilution of a biological test sample from a subject, the methodcomprising horizontally translating the level of at least one analytefrom a biological test sample to a composite dilution model developedfor the at least one analyte, thereby determining the relative dilutionof the biological test sample from the subject.
 44. The method of anyone of claims 40 to 43, comprising horizontally translating the level ofat least 2, at least 3, at least 4, at least 5, at least 6, at least 7,at least 8, at least 9, at least 10, at least 11, at least 12, at least13, at least 14, at least 15, at least 16, at least 17, at least 18, atleast 19, at least 20, at least 21, at least 22, at least 23, at least24, at least 25, at least 26, at least 27, at least 28, at least 29, atleast 30, at least 50, at least 75, at least 100, at least 150, or atleast 200 different analytes from the biological test sample to therespective composite dilution model developed for each of the differentanalytes to determine the relative dilution of the biological testsample for each of the different analytes, and using the relativedilution for each of the different analytes to determine the relativedilution of the biological test sample.
 45. The method of claim 44,wherein the relative dilution of the biological test sample is derivedfrom the central tendency of the relative dilutions of each of thedifferent analytes.
 46. The method of claim 44 or claim 45, wherein therelative dilution of the biological test sample is derived from themedian, mean or mode of the relative dilutions of each of the differentanalytes.
 47. The method of any one of claims 43 to 46, wherein thecomposite dilution model was developed using the method of any one ofclaims 1 to
 42. 48. The method of any one of claims 43 to 48, whereinthe biological test sample and the samples used to develop the compositedilution model are the same sample type.
 49. The method of claim 41,wherein the biological test sample and the samples used to develop thecomposite dilution model are urine samples or derived from urinesamples.
 50. The method of any one of claims 40 to 49, furthercomprising calculating the relative dilution of the biological testsample with the derived relative dilution factor.
 51. The method of anyone of the preceding claims, wherein each analyte is a target protein.52. A computer system, comprising: a non-transitory memory storinginstructions; and one or more hardware processors coupled to thenon-transitory memory and configured to read the instructions from thenon-transitory memory to cause the computer system to perform operationscomprising: receiving analyte measurement data from a plurality ofdifferent biological samples; analyzing respective analyte measurementsfor each one of a plurality of selected analytes in the analytemeasurement data; and generating a composite dilution model for each oneof the selected analytes based on the analyzing.
 53. The computer systemof claim 52, wherein the analyzing comprises: determining, for eachselected analyte, the biological sample with the greatest lineardilution range.
 54. The computer system of claim 53, wherein theanalyzing further comprises: generating, for each selected analyte, areference dilution model based on the determined greatest linear rangefor the respective selected analyte.
 55. The computer system of claim54, wherein the analyzing further comprises: translating, for eachselected analyte, the analyte measurement data associated with therespective selected analyte based on the reference dilution modelgenerated for the respective selected analyte.
 56. The computer systemof any one of claims 52 to 55, wherein the biological samples are urinesamples.
 57. The computer system of any one of claims 52 to 56, whereinthe analyte measurement data comprises relative fluorescence unit (RFU)measurements.
 58. A non-transitory computer-readable medium comprisingcomputer-readable instructions which, when executed by a processingdevice, cause the processing device to perform operations comprising:receiving analyte measurement data from a plurality of differentbiological samples; analyzing respective analyte measurements for eachone of a plurality of selected analytes in the analyte measurement data;and generating a composite dilution model for each one of the selectedanalytes based on the analyzing.
 59. A computer-implemented method forgenerating a composite dilution model for biological material,comprising: receiving, by one or more processing devices, analytemeasurement data from a plurality of different biological samples;analyzing, by one or more of the processing devices, respective analytemeasurements for each one of a plurality of selected analytes in theanalyte measurement data; and generating, by one or more of theprocessing devices, a composite dilution model for each one of theselected analytes based on the analyzing.
 60. A computer system,comprising: a non-transitory memory storing instructions; and one ormore hardware processors coupled to the non-transitory memory andconfigured to read the instructions from the non-transitory memory tocause the computer system to perform operations comprising: receivinganalyte measurement data for analytes in a biological sample; selectinga plurality of the analytes for determining a predicted relativedilution of the biological sample; receiving a composite dilution modelgenerated for each respective one of the selected analytes; determining,for each one of the selected analytes, a predicted relative dilutionvalue based on a corresponding composite dilution model generated forthe respective selected analyte; and determining the predicted relativedilution of the biological sample based on the determined predictedrelative dilution values for the selected analytes.